Note : Les descriptions sont présentées dans la langue officielle dans laquelle elles ont été soumises.
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INFLOW DETECTION USING DTS FEATURES
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] Not applicable.
STATEMENT REGARDING FEDERALLY SPONSORED
RESEARCH OR DEVELOPMENT
[0002] Not applicable.
BACKGROUND
[0003] To obtain hydrocarbons from subterranean formations, wellbores are
drilled from the
surface to access the hydrocarbon-bearing formation. After drilling a wellbore
to the desired
depth, a production string is installed in the wellbore to produce the
hydrocarbons from one or
more production zones of the formation to the surface. The production of the
fluids can be
detected at the wellhead based on total flow of fluid. However, it can be
difficult to determine
where the fluid is in flowing into the wellbore when multiple productions
zones are present
BRIEF SUMMARY
[0004] In some embodiments, a method of determining fluid inflow locations
comprises
determining a plurality of temperature features from a distributed temperature
sensing signal
originating in a wellbore, using the plurality of temperature features in a
fluid inflow identification
model, and determining the presence of fluid inflow at one or more locations
along the wellbore
based on an output from the fluid inflow identification model.
[0005] In some embodiments, a method of determining fluid inflow locations
comprises
determining a plurality of temperature features from a distributed temperature
sensing signal
originating in a wellbore, and determining the presence of fluid inflow at one
or more locations
along the wellbore using the plurality of temperature features. The plurality
of temperature
features comprise at least two of: a depth derivative of temperature with
respect to depth, a
temperature excursion measurement, a baseline temperature excursion, or a peak-
to-peak value.
[0006] In some embodiments, a system of determining fluid inflow locations
within a wellbore
comprises a processor, a memory, and an analysis program stored in the memory.
The analysis
program is configured, when executed on the processor, to receive a
distributed temperature
sensing signal, wherein the distributed temperature sensing signal originates
within the wellbore,
determine a plurality of temperature features from the distributed temperature
sensing signal, use
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the plurality of temperature features in a fluid inflow identification model,
mid determine the
presence of fluid inflow at one or more locations along the wellbore based on
an output from the
fluid inflow identification model.
[0007] Embodiments described herein comprise a combination of features and
characteristics
intended to address various shortcomings associated with certain prior
devices, systems, and
methods. The foregoing has outlined rather broadly the features and technical
characteristics of the
disclosed embodiments in order that the detailed description that follows may
be better understood.
The various characteristics and features described above, as well as others,
will be readily apparent
to those skilled in the art upon reading the following detailed description,
and by referring to the
accompanying drawings. It should be appreciated that the conception and the
specific
embodiments disclosed may be readily utilized as a basis for modifying or
designing other
structures for carrying out the same purposes as the disclosed embodiments. It
should also be
realized that such equivalent constructions do not depart from the spirit and
scope of the principles
disclosed herein.
BRIEF DESCRIPTION OF THE DRAWINGS
[0008] For a detailed description of various exemplary embodiments, reference
will now be
made to the accompanying drawings in which:
[0009] FIG. 1 is a schematic, cross-sectional illustration of a downhole
wellbore environment
according to some embodiments;
[0010] FIGS. 2A and 2B are a schematic, cross-sectional views of embodiments
of a well with a
wellbore tubular having an optical fiber inserted therein according to some
embodiments;
[0011] FIG. 3 is a schematic view of an embodiment of a wellbore tubular with
fluid inflow and
sand ingress according to some embodiments;
[0012] FIG. 4 is a flow chart of a method for determining fluid inflow
locations within a
wellbore according to some embodiments;
[0013] FIG. 5 is a flow diagram of a method of determining fluid inflow rates
at one or more
locations within a wellbore according to some embodiments;
[0014] FIG. 7 schematically illustrates a computer that may be used to carry
out various methods
according to some embodiments.
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DETAILED DESCRIPTION
[0015] The following discussion is directed to various exemplary embodiments.
However, one of
ordinary skill in the art will understand that the examples disclosed herein
have broad application,
and that the discussion of any embodiment is meant only to be exemplary of
that embodiment, and
not intended to suggest that the scope of the disclosure, including the
claims, is limited to that
embodiment.
[0016] The drawing figures are not necessarily to scale. Certain features and
components herein
may be shown exaggerated in scale or in somewhat schematic form and some
details of
conventional elements may not be shown in interest of clarity and conciseness.
[0017] Unless otherwise specified, any use of any form of the terms "connect,"
"engage,"
"couple," "attach," or any other term describing an interaction between
elements is not meant to
limit the interaction to direct interaction between the elements and may also
include indirect
interaction between the elements described. In the following discussion and in
the claims, the terms
"including" and "comprising" are used in an open-ended fashion, and thus
should be interpreted to
mean "including, but not limited to. . . ". Reference to up or down will be
made for purposes of
description with "up," -upper," "upward," "upstream," or "above" meaning
toward the surface of
the wellbore and with "down," "lower," "downward," "downstream," or "below"
meaning toward
the terminal end of the well, regardless of the wellbore orientation.
Reference to inner or outer will
be made for purposes of description with "in," "inner," or "inward" meaning
towards the central
longitudinal axis of the wellbore and/or wellbore tubular, and "out," "outer,"
or "outward"
meaning towards the wellbore wall. As used herein, the term "longitudinal" or
"longitudinally"
refers to an axis substantially aligned with the central axis of the wellbore
tubular, and "radial" or
"radially" refer to a direction perpendicular to the longitudinal axis. The
various characteristics
mentioned above, as well as other features and characteristics described in
more detail below, will
be readily apparent to those skilled in the art with the aid of this
disclosure upon reading the
following detailed description of the embodiments, and by referring to the
accompanying
drawings.
[0018] As utilized herein, a 'fluid inflow event' includes fluid inflow (e.g.,
any fluid inflow
regardless of composition thereof), gas phase inflow, aqueous phase inflow,
and/or hydrocarbon
phase inflow. The fluid can comprise other components such as solid
particulate matter (e.g., sand,
etc.) in some embodiments, as discussed in more detail herein.
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[0019] Disclosed herein are systems and methods for detecting and/or
characterizing fluid inflow
event locations and an estimation of the inflow quantities within a
subterranean wellbore, so that
a wellbore operator may more effectively control the fluid production from the
wellbore. The
systems allow for an identification of the inflow locations as well as the
inflow rates at those
locations using temperature features derived from temperature measurements as
well as
frequency domain features obtained from acoustic signals.
[0020] In some embodiments, temperature features can be determined from
temperature
measurements taken along the length of the wellbore. The temperature
measurements can be
used in a fluid inflow identification model that can provide an output
indicative of fluid inflow
event locations along the wellbore. This can allow those locations with inflow
to be identified
using temperature based measurements from the wellbore. When combined with a
distributed
temperature sensing system that can provide distributed and continuous
temperature
measurements, the systems can allow for fluid inflow locations to be tracked
through time.
[0021] The systems described herein also allow for various frequency domain
features to be
obtained from an acoustic signal originating within the wellbore. The acoustic
signals can be
obtained using a distributed acoustic sensing (DAS) system that allows for
continuous and
distributed acoustic sensing. The acoustic signals can be taken along the same
portions of the
wellbore as the temperature measurements, thereby allowing for information
about the wellbore,
and more particularly, fluid inflow events, to be determined using both the
temperature features
and the frequency domain features. In some embodiments, a fluid inflow model
can be
developed and used with one or more frequency domain features that can allow
for the prediction
of fluid inflow rates for one or more fluids and/or fluid phases.
[0022] When used together, the fluid inflow identification model can allow the
inflow locations
to be determined using temperature features, and the fluid inflow model can
allow for fluid
inflow rates to be determined at the one or more fluid inflow locations. The
fluid inflow rates as
determined from the fluid inflow model can be used to verify or validate the
fluid inflow
locations as determined from the fluid inflow identification model. This can
help to provide an
improved fluid inflow location determination for use in managing the
production of fluids from
the wellbore.
[0023] The fluid inflow model can also be used to determine the fluid inflow
rates of one or
more fluids, fluid phases (e.g., combined gas flow, combined liquid flow,
etc.), or both from one
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or more locations along the wellbore. The processing can be combined such that
the fluid inflow
model may only be executed at the one or more locations as identified using
the temperature
features with the fluid inflow identification model. The resulting fluid
inflow rates as
determined from the fluid inflow model can be used to provide an indication of
the fluid inflow
rates at the one or more locations. The resulting fluid inflow rate can also
be normalized across
the one or more locations to provide a relative contribution to the total
fluid inflow rates at the
one or more locations. This information can be used along with actual fluid
production rates to
provide an improved determination of the actual fluid inflow rates at one or
more locations
within the wellbore. This can allow the well operater to have near real time
information on the
wellbore to more effectively manage the production of fluids from the
wellbore.
[0024] Referring now to FIG. 1, a schematic, cross-sectional illustration of a
downhole wellbore
operating environment 101 according to some embodiments is shown. More
specifically,
environment 101 includes a wellbore 114 traversing a subterranean formation
102, casing 112
lining at least a portion of wellbore 114, and a tubular 120 extending through
wellbore 114 and
casing 112. A plurality of completion assemblies such as spaced screen
elements or assemblies
118 may be provided along tubular 120 at one or more production zones 104a,
104b within the
subterranean formation 102_ In particular, two production zones 104a, 104b are
depicted within
subterranean formation 102 of FIG. 1; however, the precise number and spacing
of the
production zones 104a, 104b may be varied in different embodiments. The
completion
assemblies can comprise flow control devices such as sliding sleeves,
adjustable chokes, and/or
inflow control devices to allow for control of the flow from each production
zone. The
production zones 104a, 104b may be layers, zones, or strata of formation 102
that contain
hydrocarbon fluids (e.g., oil, gas, condensate, etc.) therein.
[0025] In addition, a plurality of spaced zonal isolation devices 117 and
gravel packs 122 may be
provided between tubular 120 and the sidewall of wellbore 114 at or along the
interface of the
wellbore 114 with the production zones 104a, 104b. In some embodiments, the
operating
environment 101 includes a workover and/or drilling rig positioned at the
surface and extending
over the wellbore 114. While FIG. 1 shows an example completion configuration
in FIG. 1, it
should be appreciated that other configurations and equipment may be present
in place of or in
addition to the illustrated configurations and equipment. For example,
sections of the wellbore
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114 can be completed as open hole completions or with gravel packs without
completion
assemblies.
[0026] In general, the wellbore 114 can be formed in the subterranean
formation 102 using any
suitable technique (e.g., drilling). The wellbore 114 can extend substantially
vertically from the
earth's surface over a vertical wellbore portion, deviate from vertical
relative to the earth's
surface over a deviated wellbore portion, and/or transition to a horizontal
wellbore portion. In
general, all or portions of a wellbore may be vertical, deviated at any
suitable angle, horizontal,
and/or curved. In addition, the wellbore 114 can be a new wellbore, an
existing wellbore, a
straight wellbore, an extended reach wellbore, a sidetracked wellbore, a multi-
lateral wellbore,
and other types of wellbores for drilling and completing one or more
production zones. As
illustrated, the wellbore 114 includes a substantially vertical producing
section 150 which
includes the production zones 104a, 104b. In this embodiment, producing
section 150 is an
open-hole completion (i.e., casing 112 does not extend through producing
section 150).
Although section 150 is illustrated as a vertical and open-hole portion of
wellbore 114 in FIG. 1,
embodiments disclosed herein can be employed in sections of wellbores having
any orientation,
and in open or cased sections of wellbores. The casing 112 extends into the
wellbore 114 from
the surface and can be secured within the wellbore 114 with cement 111.
[0027] The tubular 120 may comprise any suitable downhole tubular or tubular
string (e.g., drill
string, casing, liner, jointed tubing, and/or coiled tubing, etc.), and may be
inserted within
wellbore 114 for any suitable operation(s) (e.g., drilling, completion,
intervention, workover,
treatment, production, etc.). In the embodiment shown in Figure 2, the tubular
120 is a
completion assembly string. In addition, the tubular 120 may be disposed
within in any or all
portions of the wellbore 114 (e.g., vertical, deviated, horizontal, and/or
curved section of
wellbore 114).
[0028] In this embodiment, the tubular 120 extends from the surface to the
production zones
104aõ 104b and generally provides a conduit for fluids to travel from the
formation 102
(particularly from production zones 104a, 104b) to the surface. A completion
assembly
including the tubular 120 can include a variety of other equipment or downhole
tools to facilitate
the production of the formation fluids from the production zones. For example,
zonal isolation
devices 117 can be used to isolate the production zones 104aõ 104b within the
wellbore 114. In
this embodiment, each zonal isolation device 117 comprises a packer (e.g.,
production packer,
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gravel pack packer, frac-pac packer, etc.). The zonal isolation devices 117
can be positioned
between the screen assemblies 118, for example, to isolate different gravel
pack zones or
intervals along the wellbore 114 from each other. In general, the space
between each pair of
adjacent zonal isolation devices 117 defines a production interval, and each
production interval
may correspond with one of the production zones 104a, 104b of subterranean
formation 102.
[00291 The screen assemblies 118 provide sand control capability. In
particular, the sand control
screen elements 118, or other filter media associated with wellbore tubular
120, can be designed
to allow fluids to flow therethrough but restrict and/or prevent particulate
matter of sufficient
size from flowing therethrough. The screen assemblies 118 can be of any
suitable type such as
the type known as "wire-wrapped", which are made up of a wire closely wrapped
helically about
a wellbore tubular, with a spacing between the wire wraps being chosen to
allow fluid flow
through the filter media while keeping particulates that are greater than a
selected size from
passing between the wire wraps. Other types of filter media can also be
provided along the
tubular 120 and can include any type of structures commonly used in gravel
pack well
completions, which permit the flow of fluids through the filter or screen
while restricting and/or
blocking the flow of particulates (e.g. other commercially-available screens,
slotted or perforated
liners or pipes; sintered-metal screens; sintered-sized, mesh screens;
screened pipes; prepacked
screens and/or liners; or combinations thereof). A protective outer shroud
having a plurality of
perforations therethrough may be positioned around the exterior of any such
filter medium.
[0030] The gravel packs 122 can be formed in the annulus 119 between the
screen elements 118
(or tubular 120) and the sidewall of the wellbore 114 in an open hole
completion. In general, the
gravel packs 122 comprise relatively coarse granular material placed in the
annulus to form a
rough screen against the ingress of sand into the wellbore while also
supporting the wellbore
wall. The gravel pack 122 is optional and may not be present in all
completions.
[0031] In some embodiments, one or more of the completion assemblies can
comprise flow
control elements such as sliding sleeves, chokes, valves, or other types of
flow control devices
that can control the flow of a fluid from an individual production zone or a
group of production
zones. The force on the production face can then vary based on the type of
completion within
the wellbore and/or each production zone (e.g., in a sliding sleeve
completion, open hole
completion, gravel pack completion, etc.). In some embodiments, a sliding
sleeve or other flow
controlled production zone can experience a force on the production face that
is relatively
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uniform within the production zone, and the force on the production face can
be different
between each production zone. For example, a first production zone can have a
specific flow
control setting that allows the production rate from the first zone to be
different than the
production rate from a second production zone. Thus, the choice of completion
type (e.g., which
can be specified in a completion plan) can affect on the need for or the
ability to provide a
different production rate within different production zones.
[0032] Referring still to FIG. 1, a monitoring system 110 can comprise an
acoustic monitoring
system and/or a temperature monitoring system. The monitoring system 1110 can
be positioned
in the wellbore 114. As described herein, the monitoring system 110 may be
utilized to detect or
monitor fluid inflow event(s) into the wellbore 114. The various monitoring
systems (e.g.,
acoustic monitoring systems, temperature monitoring systems, etc.) may be
referred to herein as
an "inflow detection system," and/or an "inflow monitoring system."
[0033] The monitoring system 110 comprises an optical fiber 162 that is
coupled to and extends
along tubular 120. In cased completions, the optical fiber 162 can be
installed between the
casing and the wellbore wall within a cement layer and/or installed within the
casing or
production tubing. Referring briefly to FIGS. 2A and 2B, optical fiber 162 of
the monitoring
system 110 may be coupled to an exterior of tubular 120 (e.g., such as shown
in FIG. 2B) or an
interior of tubular (e.g., such as shown in FIG. 2A). When the optical fiber
162 is coupled to the
exterior of the tubular 120, as depicted in the embodiment of FIG. 2B, the
optical fiber 162 can
be positioned within a control line, control channel, or recess in the tubular
120. In some
embodiments an outer shroud contains the tubular 120 and protects the optical
fiber 162 during
installation. A control line or channel can be formed in the shroud and the
optical fiber 162 can
be placed in the control line or channel (not specifically shown in FIGS_ 2A
and 2B).
[0034] Referring again to FIG. 1, generally speaking, during operation of the
monitoring system,
an optical backscatter component of light injected into the optical fiber 162
may be used to detect
various conditions incident on the optical fiber such as acoustic
perturbations (e.g., dynamic
strain), temperature, static strain, and the like along the length of the
optical fiber 162. The light
can be generated by a light generator or source 166 such as a laser, which can
generate light
pulses. The light used in the system is not limited to the visible spectrum,
and light of any
frequency can be used with the systems described herein. Accordingly, the
optical fiber 162 acts
as the sensor element with no additional transducers in the optical path, and
measurements can
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be taken along the length of the entire optical fiber 162. The measurements
can then be detected
by an optical receiver such as sensor 164 and selectively filtered to obtain
measurements from a
given depth point or range, thereby providing for a distributed measurement
that has selective
data for a plurality of zones (e.g., production zones 104a, 104b) along the
optical fiber 162 at any
given time. For example, time of flight measurements of the backscattered
light can be used to
identify individual zones or measurement lengths of the fiber optic 162. In
this manner, the
optical fiber 162 effectively functions as a distributed array of sensors
spread over the entire
length of the optical fiber 162, which typically across production zones 104a,
104b within the
wellbore 114.
[00351 The light backscattered up the optical fiber 162 as a result of the
optical backscatter can
travel back to the source, where the signal can be collected by a sensor 164
and processed (e.g.,
using a processor 168). In general, the time the light takes to return to the
collection point is
proportional to the distance traveled along the optical fiber 162, thereby
allowing time of flight
measurements of distance along the optical fiber. The resulting backscattered
light arising along
the length of the optical fiber 162 can be used to characterize the
environment around the optical
fiber 162. The use of a controlled light source 166 (e.g., having a controlled
spectral width and
frequency) may allow the backscatter to be collected and any parameters and/or
disturbances
along the length of the optical fiber 162 to be analyzed. In general, the
various parameters
and/or disturbances along the length of the optical fiber 162 can result in a
change in the
properties of the backscattered light.
100361 An acquisition device 160 may be coupled to one end of the optical
fiber 162 that
comprises the sensor 164, light generator 166, a processor 168, and a memory
170. As discussed
herein, the light source 166 can generate the light (e.g., one or more light
pulses), and the sensor
164 can collect and analyze the backscattered light returning up the optical
fiber 162. In some
contexts, the acquisition device 160 (which comprises the light source 166 and
the sensor 164 as
noted above), can be referred to as an interrogator. The processor 168 may be
in signal
communication with the sensor 164 and may perform various analysis steps
described in more
detail herein. While shown as being within the acquisition device 160, the
processor 168 can
also be located outside of the acquisition device 160 including being located
remotely from the
acquisition device 160. The sensor 164 can be used to obtain data at various
rates and may
obtain data at a sufficient rate to detect the acoustic signals of interest
with sufficient bandwidth.
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While described as a sensor 164 in a singular sense, the sensor 164 can
comprise one or more
photodetectors or other sensors that can allow one or more light beams and/or
backscattered light
to be detected for further processing. In an embodiment, depth resolution
ranges in a range of
from about 1 meter to about 10 meters, or less than or equal to about 10, 9,
8, 7, 6, 5, 4, 3, 2, or 1
meter can be achieved. Depending on the resolution needed, larger averages or
ranges can be
used for computing purposes. When a high depth resolution is not needed, a
system may have a
wider resolution (e.g., which may be less expensive) can also be used in some
embodiments.
Data acquired by the monitoring system 110 (e.g., via fiber 162, sensor 164,
etc.) may be stored
on memory 170.
[0037] The monitoring system 110 can be used for detecting a variety of'
parameters and/or
disturbances in the wellbore including being used to detect temperatures along
the wellbore,
acoustic signals along the wellbore, static strain and/or pressure along the
wellbore, or any
combination thereof.
[0038] In some embodiments, the monitoring system 110 can be used to detect
temperatures
within the wellbore. The temperature monitoring system can include a
distributed temperature
sensing (DTS) system. A DTS system can rely on light injected into the optical
fiber 162 along
with the reflected signals to determine a temperature and/or strain based on
optical time-domain
reflectometry. In order to obtain DTS measurements, a pulsed laser from the
light generator 166
can be coupled to the optical fiber 162 that serves as the sensing element.
The injected light can
be backscattered as the pulse propagates through the optical fiber 162 owing
to density and
composition as well as to molecular and bulk vibrations. A portion of the
backscattered light can
be guided back to the acquisition device 160 and split off by a directional
coupler to a sensor
164. It is expected that the intensity of the backscattered light decays
exponentially with time. As
the speed of light within the optical fiber 162 is known, the distance that
the light has passed
through the optical fiber 162 can be derived using time of flight
measurements.
[0039] In both distributed acoustic sensing (DAS) and DTS systems, the
backscattered light
includes different spectral components which contain peaks that are known as
Rayleigh and
Brillouin peaks and Raman bands. The Rayleigh peaks are independent of
temperature and can
be used to determine the DAS components of the backscattered light. The Raman
spectral bands
are caused by thermally influenced molecular vibrations. The Raman spectral
bands can then be
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used to obtain information about distribution of temperature along the length
of the optical fiber
162 disposed in the wellbore.
[0040] The Raman bacicscattered light has two components, Stokes and Anti-
Stokes, one being
only weakly dependent on temperature and the other being greatly influenced by
temperature.
The relative intensities between the Stokes and Anti-Stokes components are a
function of
temperature at which the backscattering occurred. Therefore, temperature can
be determined at
any point along the length of the optical fiber 162 by comparing at each point
the Stokes and
Anti-stokes components of the light backscattered from the particular point.
The Brillouin peaks
may be used to monitor strain along the length of the optical fiber 162.
[00411 The DTS system can then be used to provide a temperature measurement
along the length
of the wellbore during the production of fluids, including fluid inflow
events. The DTS system
can represent a separate system from the DAS system or a single common system,
which can
comprise one or more acquisition devices in some embodiments. In some
embodiments, a
plurality of fibers 162 are present within the wellbore, and the DAS system
can be coupled to a
first optical fiber and the DTS system can be coupled to a second, different,
optical fiber.
Alternatively, a single optical fiber can be used with both systems, and a
time division
multiplexing or other process can be used to measure both DAS and DTS on the
same optical
fiber.
100421 In an embodiment, depth resolution for the DTS system can range from
about 1 meter to
about 10 meters, or less than or equal to about 10, 9, 8, 7, 6, 5, 4, 3, 2, or
1 meter can be
achieved. Depending on the resolution needed, larger averages or ranges can be
used for
computing purposes. When a high depth resolution is not needed, a system may
have a wider
resolution (e.g., which may be less expensive) can also be used in some
embodiments. Data
acquired by the DTS system 110 (e.g., via fiber 162, sensor 164, etc.) may be
stored on memory
170.
100431 While the temperature monitoring system described herein can use a DTS
system to
acquire the temperature measurements for a location or depth range in the
wellbore 114, in
general, any suitable temperature monitoring system can be used. For example,
various point
sensors, thermocouples, resistive temperature sensors, or other sensors can be
used to provide
temperature measurements at a given location based on the temperature
measurement processing
described herein. Further, an optical fiber comprising a plurality of point
sensors such as Bragg
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gratings can also be used. As described herein, a benefit of the use of the
DTS system is that
temperature measurements can be obtained across a plurality of locations
and/or across a
continuous length of the wellbore 114 rather than at discrete locations.
[0044] The monitoring system 110 can comprise an acoustic monitoring system to
monitor
acoustic signals within the wellbore. The acoustic monitoring system can
comprise a DAS based
system, though other types of acoustic monitoring systems, including other
distributed
monitoring systems, can also be used.
[0045] During operation of a DAS system an optical backscatter component of
light injected
into the optical fiber 162 (e.g., Rayleigh backscatter) may be used to detect
acoustic
perturbations (e.g., dynamic strain) along the length of the fiber 162. The
light backscattered up
the optical fiber 162 as a result of the optical backscatter can travel back
to the source, where the
signal can be collected by a sensor 164 and processed (e.g., using a processor
168) as described
herein. In general, any acoustic or dynamic strain disturbances along the
length of the optical
fiber 162 can result in a change in the properties of the backscattered light,
allowing for a
distributed measurement of both the acoustic magnitude (e.g., amplitude),
frequency and, in
some cases, of the relative phase of the disturbance. Any suitable detection
methods including
the use of highly coherent light beams, compensating interferometers, local
oscillators, and the
like can be used to produce one or more signals that can be processed to
determine the acoustic
signals or strain impacting the optical fiber along its length.
[0046] While the system 101 described herein can be used with a DAS system
(e.g.. DAS system
110) to acquire an acoustic signal for a location or depth range in the
wellbore 114, in general,
any suitable acoustic signal acquisition system can be used in performing
embodiments of
method 10 (see e.g., FIG.. 1). For example, various microphones, geophones,
hydrophones, or
other sensors can be used to provide an acoustic signal at a given location
based on the acoustic
signal processing described herein. Further, an optical fiber comprising a
plurality of point
sensors such as Bragg gratings can also be used. As described herein, a
benefit of the use of the
DAS system 110 is that an acoustic signal can be obtained across a plurality
of locations and/or
across a continuous length of the wellbore 114 rather than at discrete
locations.
[0047] The monitoring system 110 can be used to generate temperature
measurements and/or
acoustic measurements along the length of the wellbore. The resulting
measurements can be
processed to obtain various temperature and/or acoustic based features that
can then be used to
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identify inflow locations, identify in flowing fluid phases, and/or quantify
the rate of fluid inflow.
Each of the specific types of features obtained from the monitoring system are
described in more
detail below.
[0048] Fluid can be produced into the wellbore 114 and into the completion
assembly string.
During operations, the fluid flowing into the wellbore may comprise
hydrocarbon fluids, such as,
for instance hydrocarbon liquids (e.g., oil), gases (e.g., natural gas such as
methane, ethane, etc.),
and/or water, any of which can also comprise particulates such as sand.
However, the fluid
flowing into the tubular may also comprise other components, such as, for
instance steam,
carbon dioxide, and/or various multiphase mixed flows. The fluid flow can
further be time
varying such as including slugging, bubbling, or time altering flow rates of
different phases. The
amounts or flow rates of these components can vary over time based on
conditions within the
formation 102 and the wellbore 114. Likewise, the composition of the fluid
flowing into the
tubular 120 sections throughout the length of the entire production string
(e.g., including the
amount of sand contained within the fluid flow) can vary significantly from
section to section at
any given time.
[0049] As the fluid enters the wellbore 114, the fluid can create acoustic
signals and temperature
changes that can be detected by the monitoring system such as the DTS system
and/or the DAS
systems as described herein. With respect to the temperature variations, the
temperature changes
can result from various fluid effects within the wellbore such as cooling
based on gas entering
the wellbore, temperature changes resulting from liquids entering the
wellbore, and various flow
related temperature changes as a result of the fluids passing through the
wellbore. For example,
as fluids enter the wellbore, the fluids can experience a sudden pressure
drop, which can result in
a change in the temperature. The magnitude of the temperature change depends
on the phase and
composition of the inflowing fluid, the pressure drop, and the pressure and
temperature
conditions. The other major thermodynamic process that takes place as the
fluid enters the well
is thermal mixing which results from the heat exchange between the fluid body
that flows into
the wellbore and the fluid that is already flowing in the wellbore. As a
result, inflow of fluids
from the reservoir into the wellbore can cause a deviation in the flowing well
temperature
profile.
[0050] By obtaining the temperature in the wellbore, a number of temperature
features can be
obtained from the temperature measurements. The temperature features can
provide an
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indication of one or more temperature trends at a given location in the
wellbore during a
measurement period. The resulting features can form a distribution of
temperature results that
can then be used with various models to identify one or more events within the
wellbore at the
location.
[0051] The temperature measurements can represent output values from the DTS
system, which
can be used with or without various types of pre-processing such as noise
reduction, smoothing,
and the like. When background temperature measurements are used, the
background
measurement can represent a temperature measurement at a location within the
wellbore taken in
the absence of the flow of a fluid. For example, a temperature profile along
the wellbore can be
taken when the well is initially formed and/or the wellbore can be shut in and
allowed to
equilibrate to some degree before measuring the temperatures at various points
in the wellbore.
The resulting background temperature measurements or temperature profile can
then be used in
determining the temperature features in some embodiments.
100521 In general, the temperature features represent statistical variations
of the temperature
measurements through time and/or depth. For example, the temperature features
can represent
statistical measurements or functions of the temperature within the wellbore
that can be used
with various models to determine whether or not fluid inflow events have
occurred. The
temperature features can be determined using various functions and
transformations, and in some
embodiments can represent a distribution of results. In some embodiments, the
temperature
features can represent a normal or Gaussian distribution. The resulting
distributions can then be
used with models such as multivariate models to determine the presence of the
fluid inflow
events.
[00531 In some embodiments, the temperature features can include various
features including,
but not limited to, a depth derivative of temperature with respect to depth, a
temperature
excursion measurement, a baseline temperature excursion, a peak-to-peak value,
and
combinations thereof.
[00541 A depth derivative of temperature with respect to depth. This feature
can be determined
by taking the temperature measurements along the wellbore and smoothing the
measurements.
Smoothing can comprise a variety of steps including filtering the results, de-
noising the results,
or the like. In some embodiments, the temperature measurements can be median
filtered within
a given window to smooth the measurements. Once smoothed, the change in the
temperature
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with depth can be determined. In some embodiments, this can include taking a
derivative of the
temperature measurements with respect to depth along the longitudinal axis of
the wellbore. The
depth derivative of temperature values can then be processed, and the
measurement with a zero
value (e.g., representing a point of no change in temperature with depth) that
have preceding and
proceeding values that are non-zero and have opposite signs in depth (e.g.,
zero below which the
value is negative and above positive or vice versa) can have the values assign
to the nearest
value. This can then result in a set of measurements representing the depth
derivative of
temperature with respect to depth.
[0055] In some embodiments, the temperature features can comprise a
temperature excursion
measurement. The temperature excursion measurement can comprise a difference
between a
temperature reading at a first depth and a smoothed temperature reading over a
depth range,
where the first depth is within the depth range. In some embodiments, the
temperature excursion
measurement can represent a difference between de-trended temperature
measurements over an
interval and the actual temperature measurements within the interval. For
example, a depth
range can be selected within the wefibore. The temperature readings within a
time window can
be obtained within the depth range and de-trended or smoothed. In some
embodiments, the de-
trending or smoothing can include any of those processes described above, such
as using median
filtering of the data within a window within the depth range. For median
filtering, the larger the
window of values used, the greater the smoothing effect can be on the
measurements. For the
temperature excursion measurement, a range of windows from about 10 to about
100 values, or
between about 20-60 values (e.g., measurements of temperature within the depth
range) can be
used to median filter the temperature measurements. A difference can then be
taken between the
temperature measurement at a location and the de-trended (e.g., median
filtered) temperature
values. The temperature measurements at a location can be within the depth
range and the
values being used for the median filtering. This temperature feature then
represents a
temperature excursion at a location along the wellbore from a smoothed
temperature
measurement over a larger range of depths around the location in the wellbore.
100561 In some embodiments, the temperature features can comprise a baseline
temperature
excursion. The baseline temperature excursion represents a difference between
a de-trended
baseline temperature profile and the current temperature at a given depth. In
some embodiments,
the baseline temperature excursion can rely on a baseline temperature profile
that can contain or
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define the baseline temperatures along the length of the wellbore. As
described herein, the
baseline temperatures represent the temperature as measured when the wellbore
is shut in. This
can represent a temperature profile of the formation in the absence of fluid
flow. While the
wellbore may affect the baseline temperature readings, the baseline
temperature profile can
approximate a formation temperature profile. The baseline temperature profile
can be determined
when the wellbore is shut in and/or during formation of the wellbore, and the
resulting baseline
temperature profile can be used over time. If the condition of the wellbore
changes over time,
the wellbore can be shut in and a new baseline temperature profile can be
measured or
determined. It is not expected that the baseline temperature profile is re-
determined at specific
intervals, and rather it would be determined at discrete times in the life of
the wellbore. In some
embodiments, the baseline temperature profile can be re-determined and used to
determine one
or more temperature features such as the baseline temperature excursion.
[0057] Once the baseline temperature profile is obtained, the baseline
temperature measurements
at a location in the wellbore can be subtracted from the temperature
measurement detected by the
temperature monitoring system at that location to provide baseline subtracted
values. The results
can then be obtained and smoothed or de-trended. For example, the resulting
baseline subtracted
values can be median filtered within a window to smooth the data. In some
embodiments, a
window between 10 and 500 temperature values, between 50 and 400 temperature
values, or
between 100 and 300 temperature values can be used to median filter the
resulting baseline
subtracted values. The resulting smoothed baseline subtracted values can then
be processed to
determine a change in the smoothed baseline subtracted values with depth. In
some
embodiments, this can include taking a derivative of the smoothed baseline
subtracted values
with respect to depth along the longitudinal axis of the wellbore. The
resulting values can
represent the baseline temperature excursion feature.
[0058] In some embodiments, the temperature features can comprise a peak-to-
peak temperature
value. This feature can represent the difference between the maximum and
minimum values
(e.g., the range, etc.) within the temperature profile along the wellbore. In
some embodiments,
the peak-to-peak temperature values can be determined by detecting the maximum
temperature
readings (e.g., the peaks) and the minimum temperature values (e.g., the dips)
within the
temperature profile along the wellbore. The difference can then be determined
within the
temperature profile to determine peak-to-peak values along the length of the
wellbore. The
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resulting peak-to-peak values can then be processed to determine a change in
the peak-to-peak
values with respect to depth. In some embodiments, this can include taking a
derivative of the
peak-to-peak values with respect to depth along the longitudinal axis of the
wellbore. The
resulting values can represent the peak-to-peak temperature values.
[0059] Other temperature features can also be determined from the temperature
measurements.
In some embodiments, various statistical measurements can be obtained from the
temperature
measurements along the wellbore to determine one or more temperature features.
For example, a
cross-correlation of the temperature measurements with respect to time can be
used to determine
a cross-correlated temperature feature. The temperature measurements can be
smoothed as
described herein prior to determining the cross-correlation with respect to
time. As another
example, an autocorrelation measurement of the temperature measurements can be
obtained with
respect to depth. The temperature measurements can be smoothed and/or the
resulting
autocorrelation measurements can be smoothed as described herein to determine
the
autocorrelation temperature features.
[0060] In some embodiments, the temperature features can be based on dynamic
temperature
measurements rather than steady state or flowing temperature measurements. In
order to obtain
dynamic temperature measurements, a change in the operation of the wellbore
can be introduced,
and the temperature monitored using the temperature monitoring system. The
change in
conditions can be introduced by shutting in the wellbore, opening one or more
sections of the
wellbore to flow, introducing a fluid to the wellbore (e.g., injecting a
fluid), and the like. When
the wellbore is shut in from a flowing state, the temperature profile along
the wellbore may be
expected to change from the flowing profile to the baseline profile over time.
Similarly, when a
wellbore that is shut in is opened for flow, the temperature profile may
change from a baseline
profile to a flowing profile. Based on the change in the condition of the
wellbore, the
temperature measurements can change dynamically over time. In some
embodiments, this
approach can allow for a contrast in thermal conductivity to be determined
between a location or
interval having radial flow (e.g., into or out of the wellbore) to a location
or interval without
radial flow. One or more temperature features can then be determined using the
dynamic
temperature measurements. Once the temperature features are determined from
the temperature
measurements obtained from the temperature monitoring system, one or more of
the temperature
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features can be used to identify fluid inflow events within the wellbore, as
described in more
detail herein.
[00611 As described with respect to the temperature measurements, the inflow
of fluids into the
wellbore 114 can also create acoustic sounds that can be detected using the
acoustic monitoring
system such as a DAS system. Accordingly, the flow of the various fluids into
the wellbore 114
and/or through the wellbore 114 can create vibrations or acoustic sounds that
can be detected
using acoustic monitoring system. Each type of inflow event such as the
different fluid flows
and fluid flow locations can produce an acoustic signature with unique
frequency domain
features.
[0062] As used herein, various frequency domain features can be obtained from
the acoustic
signal, and in some contexts, the frequency domain features can also be
referred to herein as
spectral features or spectral descriptors. The frequency domain features are
features obtained
from a frequency domain analysis of the acoustic signals obtained within the
wellbore. The
frequency domain features can be derived from the full spectrum of the
frequency domain of the
acoustic signal such that each of the frequency domain features can be
representative of the
frequency spectrum of the acoustic signal. Further, a plurality of different
frequency domain
features can be obtained from the same acoustic signal (e.g., the same
acoustic signal at a
location or depth within the wellbore), where each of the different frequency
domain features is
representative of frequencies across the same frequency spectrum of the
acoustic signal as the
other frequency domain features. For example, the frequency domain features
(e.g., each
frequency domain feature) can be a statistical shape measurement or spectral
shape function of
the spectral power measurement across the same frequency bandwidth of the
acoustic signal.
Further, as used herein, frequency domain features can also refer to features
or feature sets
derived from one or more frequency domain features, including combinations of
features,
mathematical modifications to the one or more frequency domain features, rates
of change of the
one or more frequency domain features, and the like.
[0063] The frequency domain features can be determined by processing the
acoustic signals
from within the wellbore at one or more locations along the wellbore. As the
acoustics signals at
a given location along the wellbore contain a combination of acoustic signals,
the determination
of the frequency domain features can be used to separate and identify
individual fluid inflow
events. As an example, FIG. 3 illustrates sand 202 flowing from the formation
102 into the
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wellbore 114 and then into the tubular 120. As the sand 202 flows into the
tubular 120, it can
collide against the inner surface 204 of the tubular 120, and with the fiber
162 (e.g., in cases
where the fiber 162 is placed within the tubular 120), in a random fashion.
Without being
limited by this or any particular theory, the intensity of the collisions
depends on the effective
mass and the rate of change in the velocity of the impinging sand particles
202, which can
depend on a number of factors including, without limitation, the direction of
travel of the sand
202 in the wellbore 114 and/or tubular 120. The resulting random impacts can
produce a
random, broadband acoustic signal that can be captured on the optical fiber
162 coupled (e.g.,
strapped) to the tubular 120. The random excitation response tends to have a
broadband acoustic
signal with excitation frequencies extending up to the high frequency bands,
for example, up to
and beyond about 5 kHz depending on the size of the sand particles 202. In
general, larger
particle sizes may produce higher frequencies. The intensity of the acoustic
signal may be
proportional to the concentration of sand 202 generating the excitations such
that an increased
broad band power intensity can be expected at increasing sand 202
concentrations. In some
embodiments, the resulting broadband acoustic signals that can be identified
can include
frequencies in the range of about 5 Hz to about 10 kHz, frequencies in the
range of about 5 Hz to
about 5 kHz or about 50 Hz to about 5 kHz, or frequencies in the range of
about 500 Hz to about
5 kHz. Any frequency ranges between the lower frequencies values (e.g., 5 Hz,
50 Hz, 500 Hz,
etc.) and the upper frequency values (e.g., 10 kHz, 7 kHz, 5 kHz, etc.) can be
used to define the
frequency range for a broadband acoustic signal.
100641 In addition to the sand entering the wellbore, fluid inflow at the
location can also create
acoustic signals along with fluid 206 flowing axially or longitudinally
through the wellbore.
Background noise can also be present. Other acoustic signal sources can
include fluid flow with
or without sand 202 through the foimation 102, fluid flow with or without sand
202 through a
gravel pack 122, fluid flow with or without sand 202 within or through the
tubular 120 and/or
sand screen 118, fluid flow with sand 202 within or through the tubular 120
and/or sand screen
118, fluid flow without sand 202 into the tubular 120 and/or sand screen 118,
gas / liquid inflow,
hydraulic fracturing, fluid leaks past restrictions (e.g., gas leaks, liquid
leaks, etc.) mechanical
instrumentation and geophysical acoustic noises and potential point reflection
noise within the
fiber caused by cracks in the fiber optic cable / conduit under investigation.
The combined
acoustic signal can then be detected by the acoustic monitoring system. In
order to detect one or
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more of these events, the acoustic signal can be processed to determine one or
more frequency
domain features of the acoustic signal at a depth in the wellbore.
[0065] In order to determine the frequency domain features, an acoustic signal
can be obtained
using the acoustic monitoring system during operation of the wellbore. The
resulting acoustic
signal can be optionally pre-processed using a number of steps. Depending on
the type of DAS
system employed, the optical data may or may not be phase coherent and may be
pre-processed
to improve the signal quality (e.g., denoised for opto-electronic noise
normalization / de-trending
single point-reflection noise removal through the use of median filtering
techniques or even
through the use of spatial moving average computations with averaging windows
set to the
spatial resolution of the acquisition unit, etc.). The raw optical data from
the acoustic sensor can
be received, processed, and generated by the sensor to produce the acoustic
signal.
[0066] In some embodiments, a processor or collection of processors (e.g.,
processor 168 in FIG.
1) may be utilized to perform the optional pre-processing steps described
herein. In an
embodiment, the noise detrended "acoustic variant" data can be subjected to an
optional spatial
filtering step following the other pre-processing steps, if present. A spatial
sample point filter
can be applied that uses a filter to obtain a portion of the acoustic signal
corresponding to a
desired depth or depth range in the wellbore. Since the time the light pulse
sent into the optical
fiber returns as backscattered light can correspond to the travel distance,
and therefore depth in
the wellbore, the acoustic data can be processed to obtain a sample indicative
of the desired
depth or depth range. This may allow a specific location within the wellbore
to be isolated for
further analysis. The pre-processing may also include removal of spurious back
reflection type
noises at specific depths through spatial median filtering or spatial
averaging techniques. This is
an optional step and helps focus primarily on an interval of interest in the
wellbore. For
example, the spatial filtering step can be used to focus on a producing
interval where there is
high likelihood of sand ingress, for example. The resulting data set produced
through the
conversion of the raw optical data can be referred to as the acoustic sample
data.
[0067] The acoustic data, including the optionally pre-processed and/or
filtered data, can be
transformed from the time domain into the frequency domain using a transform.
For example, a
Fourier transform such as a Discrete Fourier transformations (DFT), a short
time Fourier
transform (STFT), or the like can be used to transform the acoustic data
measured at each depth
section along the fiber or a section thereof into a frequency domain
representation of the signal.
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The resulting frequency domain representation of the data can then be used to
provide the data
from which the plurality of frequency domain features can be determined.
Spectral feature
extraction using the frequency domain features through time and space can be
used to determine
one or more frequency domain features.
[00681 The use of frequency domain features to identify inflow events and
locations, inflow
phase identification, and/or inflow quantities of one or more fluid phases can
provide a number
of advantages. First, the use of frequency domain features results in
significant data reduction
relative to the raw DAS data stream. Thus, a number of frequency domain
features can be
calculated and used to allow for event identification while the remaining data
can be discarded or
otherwise stored, and the remaining analysis can performed using the frequency
domain features.
Even when the raw DAS data is stored, the remaining processing power is
significantly reduced
through the use of the frequency domain features rather than the raw acoustic
data itself.
Further, the use of the frequency domain features can, with the appropriate
selection of one or
more of the frequency domain features, provide a concise, quantitative measure
of the spectral
character or acoustic signature of specific sounds pertinent to downhole fluid
surveillance and
other applications.
100691 While a number of frequency domain features can be determined for the
acoustic sample
data, not every frequency domain feature may be used to identify inflow events
and locations,
inflow phase identification, and/or inflow quantities of one or more fluid
phases. The frequency
domain features represent specific properties or characteristics of the
acoustic signals.
100701 In some embodiments, combinations of frequency domain features can be
used as the
frequency domain features themselves, and the resulting combinations are
considered to be part
of the frequency domain features as described herein_ In some embodiments, a
plurality of
frequency domain features can be transformed to create values that can be used
to define various
event signatures. This can include mathematical transformations including
ratios, equations,
rates of change, transforms (e.g., wavelets, Fourier transforms, other wave
form transforms, etc.),
other features derived from the feature set, and/or the like as well as the
use of various equations
that can define lines, surfaces, volumes, or multi-variable envelopes. The
transformation can use
other measurements or values outside of the frequency domain features as part
of the
transformation. For example, time domain features, other acoustic features,
and non-acoustic
measurements can also be used. In this type of analysis, time can also be
considered as a factor
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in addition to the frequency domain features themselves. As an example, a
plurality of
frequency domain features can be used to define a surface (e.g., a plane, a
three-dimensional
surface, etc.) in a multivariable space, and the measured frequency domain
features can then be
used to determine if the specific readings from an acoustic sample fall above
or below the
surface. The positioning of the readings relative to the surface can then be
used to determine if
the event is present or not at that location in that detected acoustic sample.
[0071] The frequency domain features can include any frequency domain features
derived from
the frequency domain representations of the acoustic data. Such frequency
domain features can
include, but are not limited to, the spectral centroid, the spectral spread,
the spectral roll-off, the
spectral skewness, the root mean square (RMS) band energy (or the normalized
sub-band
energies / band energy ratios), a loudness or total RMS energy, a spectral
flatness, a spectral
slope, a spectral kurtosis, a spectral flux, a spectral autocorrelation
function, or a normalized
variant thereof.
[0072] The spectral centroid denotes the "brightness" of the sound captured by
the optical fiber
(e.g., optical fiber 162 shown in FIG. 1) and indicates the center of gravity
of the frequency
spectrum in the acoustic sample. The spectral centroid can be calculated as
the weighted mean
of the frequencies present in the signal, where the magnitudes of the
frequencies present can be
used as their weights in some embodiments.
[0073] The spectral spread is a measure of the shape of the spectrum and helps
measure how the
spectrum is distributed around the spectral centroid. In order to compute the
spectral spread, Si,
one has to take the deviation of the spectrum from the computed centroid as
per the following
equation (all other terms defined above):
= jaimk)-ci)2xj(k) E
Eft_ xi oz) ( q. 2).
[0074] The spectral roll-off is a measure of the bandwidth of the audio
signal. The Spectral roll-
off of the ith frame, is defined as the frequency bin 'y' below which the
accumulated magnitudes
of the short-time Fourier transform reach a certain percentage value (usually
between 85% -
95%) of the overall sum of magnitudes of the spectrum.
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EL11X1(k)1 =-1 (Eq. 3),
loo
[0075] where c=85 or 95. The result of the spectral roll-off calculation is a
bin index and
enables distinguishing acoustic events based on dominant energy contributions
in the frequency
domain (e.g., between gas influx and liquid flow, etc.).
[0076] The spectral skewness measures the symmetry of the distribution of the
spectral
magnitude values around their arithmetic mean.
[0077] The RMS band energy provides a measure of the signal energy within
defined frequency
bins that may then be used for signal amplitude population. The selection of
the bandwidths can
be based on the characteristics of the captured acoustic signal. In some
embodiments, a sub-
band energy ratio representing the ratio of the upper frequency in the
selected band to the lower
frequency in the selected band can range between about 1.5:1 to about 3:1. In
some
embodiments, the sub-band energy ratio can range from about 2.5:1 to about
1.8:1, or
alternatively be about 2:1The total RMS energy of the acoustic waveform
calculated in the time
domain can indicate the loudness of the acoustic signal. In some embodiments,
the total RMS
energy can also be extracted from the temporal domain after filtering the
signal for noise.
[0078] The spectral flatness is a measure of the noisiness / tonality of an
acoustic spectrum. It
can be computed by the ratio of the geometric mean to the arithmetic mean of
the energy
spectrum value and may be used as an alternative approach to detect broad-
banded signals. For
tonal signals, the spectral flatness can be close to 0 and for broader band
signals it can be closer
to 1.
[0079] The spectral slope provides a basic approximation of the spectrum shape
by a linearly
regressed line. The spectral slope represents the decrease of the spectral
amplitudes from low to
high frequencies (e.g., a spectral tilt). The slope, the y-intersection, and
the max and media
regression error may be used as features.
[0080] The spectral kurtosis provides a measure of the flatness of a
distribution around the mean
value.
[0081] The spectral flux is a measure of instantaneous changes in the
magnitude of a spectrum.
It provides a measure of the frame-to-frame squared difference of the spectral
magnitude vector
summed across all frequencies or a selected portion of the spectrum. Signals
with slowly
varying (or nearly constant) spectral properties (e.g., noise) have a low
spectral flux, while
signals with abrupt spectral changes have a high spectral flux. The spectral
flux can allow for a
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direct measure of the local spectral rate of change and consequently serves as
an event detection
scheme that could be used to pick up the onset of acoustic events that may
then be further
analyzed using the feature set above to identify and uniquely classify the
acoustic signal.
[0082] The spectral autocorrelation function provides a method in which the
signal is shifted,
and for each signal shift (lag) the correlation or the resemblance of the
shifted signal with the
original one is computed. This enables computation of the fundamental period
by choosing the
lag, for which the signal best resembles itself, for example, where the
autocorrelation is
maximized. This can be useful in exploratory signature analysis./ even for
anomaly detection for
well integrity monitoring across specific depths where well barrier elements
to be monitored are
positioned.
[0083] Any of these frequency domain features, or any combination of these
frequency domain
features (including transformations of any of the frequency domain features
and combinations
thereof), can be used to detect inflow events and locations, inflow phase
identification, and/or
inflow quantities of one or more phases within the wellbore. In an embodiment,
a selected set of
characteristics can be used to identify the presence or absence for each
event, and/or all of the
frequency domain features that are calculated can be used as a group in
characterizing the
presence or absence of an event. The specific values for the frequency domain
features that are
calculated can vary depending on the specific attributes of the acoustic
signal acquisition system,
such that the absolute value of each frequency domain feature can change
between systems. in
some embodiments, the frequency domain features can be calculated for each
event based on the
system being used to capture the acoustic signal and/or the differences
between systems can be
taken into account in determining the frequency domain feature values for each
fluid inflow
event between or among the systems used to determine the values and the
systems used to
capture the acoustic signal being evaluated.
[0084] One or a plurality of frequency domain features can be used to identify
inflow events and
locations, inflow phase identification, and/or inflow quantities of one or
more phases. In some
embodiments, one or a plurality of frequency domain features can also be used
to detect inflow
events and locations, inflow phase identification, and/or inflow quantities of
one or more phases.
In an embodiment, one, or at least two, three, four, five, six, seven, eight,
etc. different frequency
domain features can be used to detect inflow events and locations, inflow
phase identification,
and/or inflow quantities of one or more phases. The frequency domain features
can be combined
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or transformed in order to define the event signatures for one or more events,
such as, for
instance, a fluid inflow event location or flowrate. While exemplary numerical
ranges are
provided herein, the actual numerical results may vary depending on the data
acquisition system
and/or the values can be normalized or otherwise processed to provide
different results.
[00851 The systems described herein can be used with the temperature features
and/or frequency
domain features to determine the presence of fluid inflow at one or more
locations along the
wellbore. Figure 4 illustrates a method 400 for determining fluid inflow
locations. The method
can start at step 402 with a determination of temperature features originating
within the wellbore.
As described herein, one or more fluids that can include gas, a liquid aqueous
phase, a liquid
hydrocarbon phase, and potentially other fluids as well as various
combinations thereof can enter
the wellbore at one or more locations along the wellbore. The temperature
features can then be
used to identify these inflow locations.
[0086] The temperature features can be determined using the temperature
monitoring system to
obtain temperature measurements along the length of the wellbore. In some
embodiments, a
DTS system can be used to receive distributed temperature measurement signals
from a sensor
disposed along the wellbore, such as an optical fiber. The resulting signals
from the temperature
monitoring system can be used to determine one or more temperature features as
described
herein. In some embodiments, a baseline or background temperature profile can
be used to
determine the temperature features, and the baseline temperature profile can
be obtained prior to
obtaining the temperature measurements_
[0087] In some embodiments, a plurality of temperature features can be
determined from the
temperature measurements, and the plurality of temperature features can
comprise at least two
of: a depth derivative of temperature with respect to depth, a temperature
excursion
measurement, a baseline temperature excursion, or a peak-to-peak value. Other
temperature
features can also be used in some embodiments. The temperature excursion
measurement can
comprise a difference between a temperature reading at a first depth, and a
smoothed temperature
reading over a depth range, where the first depth is within the depth range.
The baseline
temperature excursion can comprise a derivative of a baseline excursion with
depth, where the
baseline excursion can comprise a difference between a baseline temperature
profile and a
smoothed temperature profile. The peak-to-peak value can comprise a derivative
of a peak-to-peak
CA 03154435 2022-4-11
difference with depth, where the peak-to-peak difference comprises a
difference between a peak
high temperature reading and a peak low temperature reading within an
interval.
[0088] Once the temperature features are obtained, the temperature features
can be used with a
fluid inflow identification model to identify the inflow locations at step
404. In some
embodiments, the fluid inflow identification model can accept a plurality of
temperature features
as inputs. In general, the temperature features are representative of feature
at a particular
location (e.g., a depth resolution portion of the optical fiber along the
wellbore) along the
wellbore. The fluid inflow identification model can comprise one or more
models configured to
accept the temperature features as input(s) and provide an indication of
whether or not there is a
fluid inflow event at the particular location along the wellbore. The output
of the fluid inflow
identification model can be in the form of a binary yes/no result, and/or a
likelihood of a fluid
inflow event (e.g., a percentage likelihood, etc.). Other outputs providing an
indication of a fluid
inflow event are also possible. In some embodiments, the fluid inflow
identification model can
comprise a multivariate model, a machine learning model using supervised or
unsupervised
learning algorithms, or the like.
[0089] In some embodiments, the fluid inflow identification model can comprise
a multivariate
model. A multivariate model allows for the use of a plurality of variables in
a model to
determine or predict an outcome. A multivariate model can be developed using
known data on
fluid inflow events along with temperature features for those events to
develop a relationship
between the temperature features and the presence of fluid inflow at the
locations within the
available data. One or more multivariate models can be developed using data,
where each
multivariate model uses a plurality of temperature features as inputs to
determine the likelihood
of a fluid inflow event occurring at the particular location along the
wellbore.
[0090] In some embodiments, the fluid inflow identification model can comprise
one or more
multivariate models. The multivariate model can use multivariate equations,
and the multivariate
model equations can use the temperature features or combinations or
transformations thereof to
determine when a fluid inflow event is present. The multivariate model can
define a threshold,
decision point, and/or decision boundary having any type of shapes such as a
point, line, surface,
or envelope between the presence and absence of the specific fluid inflow
event. In some
embodiments, the multivariate model can be in the form of a polynomial, though
other
representations are also possible. The model can include coefficients that can
be calibrated
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based on known fluid inflow event data. While there can be variability or
uncertainty in the
resulting values used in the model, the uncertainty can be taken into account
in the output of the
model. Once calibrated or tuned, the model can then be used with the
corresponding temperature
features to provide an output that is indicative of the occurrence of a fluid
inflow event.
[0091] The multivariate model is not limited to two dimensions (e.g., two
temperature features
or two variables representing transformed values from two or more temperature
features), and
rather can have any number of variables or dimensions in defining the
threshold between the
presence or absence of the fluid inflow event. When used, the detected values
can be used in the
multivariate model, and the calculated value can be compared to the model
values. The presence
of the fluid inflow event can be indicated when the calculated value is on one
side of the
threshold and the absence of the fluid or flow regime can be indicated when
the calculated value
is on the other side of the threshold. In some embodiments, the output of the
multivariate model
can be based on a value from the model relative to a normal distribution for
the model. Thus, the
model can represent a distribution or envelope and the resulting temperature
features can be used
to define where the output of the model lies along the distribution at the
location in the wellbore.
Thus, each multivariate model can, in some embodiments, represent a specific
determination
between the presence or absence of a fluid inflow event at a specific location
in the wellbore.
Different multivariate models, and therefore thresholds, can be used for
different fluid inflow
events, and each multivariate model can rely on different temperature features
or combinations
or transformations of temperature feature& Since the multivariate models
define thresholds for
the determination and/or identification of fluid inflow events, the
multivariate models and fluid
inflow identification model using such multivariate models can be considered
to be temperature
based event signatures for each type of fluid inflow event.
[0092] In some embodiments, the fluid inflow identification model can comprise
a plurality of
models. Each of the models can use one or more of the temperature features as
inputs. The
models can comprise any suitable model that can relate one or more temperature
features to an
occurrence of a fluid inflow event (e.g., a likelihood of the event, a binary
yes/no output, etc.).
The output of each model can then be combined to form a composite or combined
output. The
combined output can then be used to determine if a fluid inflow event has
occurred, for example,
by comparing the combined output with a threshold value (e.g., a fluid inflow
threshold). The
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determination of the occurrence of a fluid inflow event can then be based on
the comparison of
the combined output with the threshold value.
[0093] As an example, the fluid inflow identification model can comprise a
plurality of
multivariate models, each using a plurality of temperature features as
described above. The
output of the multivariate models can include a percentage likelihood of the
occurrence of a fluid
inflow event at the particular location at which each model is applied. The
resulting output
values can then be used in a function such as a simple multiplication, a
weighted average, a
voting scheme, or the like to provide a combined output. The resulting output
can then be
compared to a threshold to determine if a fluid inflow event has occurred. For
example, a
combined output indicating that there is greater than a fifty percent
likelihood of a fluid inflow
event at the particular location can be taken as an indication that a fluid
inflow event has
occurred at the location of interest.
[0094] In some embodiments, the fluid inflow identification model can also
comprise other types
of models. In some embodiments, a machine learning approach comprises a
logistic regression
model. In some such embodiments, one or more temperature features can be used
to determine if
a fluid inflow event is present at one or more locations of interest. The
machine learning
approach can rely on a training data set that can be obtained from a test set-
up or obtained based
on actual temperature data from known fluid inflow events. The one or more
temperature
features in the training data set can then be used to train the fluid inflow
identification model
using machine learning, including any supervised or unsupervised learning
approach. For
example, the fluid flow model can be a neural network, a Bayesian network, a
decision tree, a
logistical regression model, a normalized logistical regression model, or the
like. In some
embodiments, the fluid inflow identification model can comprise a model
developed using
unsupervised learning techniques such a k-means clustering and the like.
[0095] In some embodiments, the model(s) can be developed and trained using a
logistic
regression model. As an example for training of a model used to determine the
presence or
absence of a fluid inflow event, the training of the model can begin with
providing the one or
more temperature features to the logistic regression model corresponding to
one or more
reference data sets in which fluid inflow events are present. Additional
reference data sets can
be provided in which fluid inflow events are not present. The one or more
temperature features
can be provided to the logistic regression model, and a first multivariate
model can be
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determined using the one or more frequency domain features as inputs. The
first multivariate
model can define a relationship between a presence and an absence of the fluid
inflow events in
the one or more fluids.
[0096] Once the model is trained, the fluid inflow identification model can be
used to determine
the presence or absence of a fluid inflow event at one or more locations along
the wellbore in
step 406. The temperature features determined for each location along the
wellbore can be used
with the fluid inflow identification model. The output of the model can
provide an indication of
the presence of a fluid inflow event at each location for which the
temperature features are
obtained. When the output indicates that a fluid inflow event has occurred at
a given location, an
output can be generated indicating the presence of the fluid inflow event. The
process can be
repeated along the length of the wellbore to provide a fluid inflow profile,
which can comprise
an indication of the fluid inflow events at one or more locations along the
wellbore.
[0097] In some embodiments, the fluid inflow event outputs from the fluid
identification model
can be presented as a profile along a wellbore on an output device. The
outputs can be presented
in the form of an inflow profile depicted along an axis with or without a well
schematic. The
inflow profile can then be used to visualize the fluid inflow locations, which
can allow for
various processes to be carried out. For example, the fluid inflow locations
can be compared to
the producing zones within a completion to understand where fluid is entering
the wellbore. in
some embodiments, fluid inflow can be detected at locations other than a
producing zone, which
may provide an indication that a remediation procedure is needed within the
wellbore.
[0098] Also disclosed herein is a process for validating the fluid inflow
locations from the fluid
inflow identification model using the temperature features and/or determining
a quantity of fluid
(e.g., a liquid) entering the wellbore at the one or more fluid inflow
locations identified by the
fluid inflow identification model. The validating model can use one or more
frequency domain
features in one or more fluid inflow models to predict a quantity or flow rate
of one or more
fluids and/or fluid phases into the wellbore. For example, the model can be
used to predict the
inflow rates of one or more fluids including a gas, an aqueous liquid, a
hydrocarbon liquid, or
another fluid within the wellbore. In some embodiments, the model can be used
to predict the
inflow rate of a fluid phase such as a gas phase and/or a liquid phase (e.g.,
including a liquid
aqueous phase and a hydrocarbon liquid phase).
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[0099] In some embodiments, the frequency domain features can be used with a
fluid inflow
model to predict a fluid inflow rate, such as a liquid flowmte into the
wellbore. The fluid inflow
model can relate a fluid inflow rate of one or more phases (e.g., including a
total liquid flow rate)
to one or more frequency domain features. In some embodiments, the fluid
inflow model can
accept one or more frequency domain features as inputs. In general, the
frequency domain
features are representative of feature at a particular location (e.g., a depth
resolution portion of
the optical fiber along the wellbore) along the wellbore. The fluid inflow
model can comprise
one or more models configured to accept the frequency domain features as
input(s) and provide
an indication of a fluid inflow rate at the location. The output of the fluid
inflow model can be in
the form of a flow rate of one or more fluids and/or fluid phases. In some
embodiments, the
fluid inflow model can comprise a multivariate model, a machine learning model
using
supervised or unsupervised learning algorithms, or the like.
[00100] In some embodiments, a fluid inflow model can be developed using a
machine learning
approach. In some such embodiments, a single frequency domain feature (e.g.,
spectral flatness,
RMS bin values, etc.) can be used to determine if fluid inflow is present at
each location of
interest. In some embodiments, the supervised learning approach can be used to
determine a
model of the inflow rate of one or more fluids and/or fluid phases, such as
gas inflow rate, a
hydrocarbon inflow rate, a water inflow rate, a total gas phase inflow rate,
and/or a total liquid
phase (e.g., a liquid aqueous phase and a liquid hydrocarbon phase) inflow
rate.
[00101] In some embodiments, the inflow model can be trained using a labeled
data set, which
can be obtained using a test apparatus such as a test flow set-up and/or field
data that is labeled
using other instrumentation to identify the fluid inflow rates. Using testing
data as an example,
the method of developing the fluid inflow model can include determining one or
more frequency
domain features from the acoustic signal for at least a portion of the data
from the plurality of
fluid inflow tests. The one or more frequency domain features can be obtained
across the portion
of the conduit where fluid inflow occurs. The fluid inflow model can then be
trained using the
frequency domain features from the inflow tests. The training of the fluid
flow model can use
machine learning, including any supervised or unsupervised learning approach.
For example, the
fluid flow model can be a neural network, a Bayesian network, a decision tree,
a logistical
regression model, a normalized logistical regression model, k-means clustering
or the like.
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1001021 In some embodiments, the fluid inflow model can be developed and
trained using a
logistic regression model. As an example for training of a model used to
determine the fluid
inflow rate, the training of the fluid flow model can begin with providing one
or more frequency
domain features to the logistic regression model corresponding to one or more
inflow tests where
known fluid inflow rates of one or more fluids and/or one or more fluid phases
have been
measured. Similarly, one or more frequency domain features can be provided to
the logistic
regression model corresponding to one or more inflow tests where no fluid
inflow is present. A
first multivariate model can be determined using the one or more frequency
domain features as
inputs. The first multivariate model can define a relationship between a
presence and an absence
of the type of fluid inflow and/or fluid phase inflow.
1001031 In the fluid flow model, the multivariate model equations can use the
frequency domain
features or combinations or transformations thereof to determine when a
specific fluid inflow
rate or fluid inflow rate for a fluid phase is present. The multivariate model
can define a
threshold, decision point, and/or decision boundary having any type of shapes
such as a point,
line, surface, or envelope between the presence and absence of the specific
fluid inflow rate or
fluid inflow rate for a phase. In some embodiments, the multivariate model can
be in the form of
a polynomial, though other representations are also possible. When models such
a neural
networks are used, the thresholds can be based on node thresholds within the
model. As noted
herein, the multivariate model is not limited to two dimensions (e.g., two
frequency domain
features or two variables representing transformed values from two or more
frequency domain
features), and rather can have any number of variables or dimensions in
defining the threshold
between the presence or absence of the fluid inflow and the specific fluid
inflow rates for one or
more fluids and/or fluid phases. Different multivariate models can be used for
inflow rate for
each fluid type and/or fluid inflow phase, and each multivariate model can
rely on different
frequency domain features or combinations or transformations of frequency
domain features.
1001041 Whether a test system or in-situ sensors are used to obtain data on
the inflow rates
(collectively referred to as "reference data"), one or more models can be
developed for the
determination of the inflow rates using the reference data. The model(s) can
be developed by
determining one or more frequency domain features from the acoustic signal for
at least a portion
of the reference data. The training of the model(s) can use machine learning,
including any
supervised or unsupervised learning approach. For example, one or more of the
model(s) can be
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a neural network, a Bayesian network, a decision tree, a logistical regression
model, a
normalized logistical regression model, k-means clustering, or the like.
[00105] The one or more frequency domain features used in the inflow model can
comprise any
frequency domain features noted hereinabove as well as combinations and
transformations
thereof. For example, In some embodiments, the one or more frequency domain
features
comprise a spectral centroid, a spectral spread, a spectral roll-off, a
spectral skewness, an RMS
band energy, a total R_MS energy, a spectral flatness, a spectral slope, a
spectral kurtosis, a
spectral flux, a spectral autocorrelation function, combinations and/or
transformations thereof, or
any normalized variant thereof_ In some embodiments, the one or more frequency
domain
features comprise a normalized variant of the spectral spread (NVSS) and/or a
normalized
variant of the spectral centroid (NVSC).
1001061 The output of the inflow model can comprise a flow rate of one or more
fluids and/or
fluid phases. For example, the total liquid inflow rate at a location can be
determined from the
inflow model. The resulting output can be compared to the output of the fluid
inflow
identification model to allow the inflow location determination to be based
both on the fluid
inflow identification model using the temperature features and the inflow
model using the
frequency domain features. The final output can be a function of both the
output from the fluid
inflow identification model and the inflow model. In some embodiments, the
outputs can be
combined as a product, weighted product, ratio, or other mathematical
combination. Other
combinations can include voting schemes, thresholds, or the like to allow the
outputs from both
models to be combined. As an example, if the output from either model is zero,
then the fluid
inflow identification at the location would also indicate that there is no
fluid inflow at the
location. In this example, one model can indicate that fluid inflow is
present, but the other model
can indicate that no fluid inflow is present. The final result can indicate
that no fluid inflow is
present. When both models indicate that fluid inflow is present, the final
combined output can
provide a positive indication of fluid inflow at the location. It is noted
that the output of the
inflow model can provide a fluid inflow rate of one or more fluids and/or
fluid phases. While
this output is distinct from the output of the fluid inflow identification
model, the two outputs
can be combined to improve the accuracy of the fluid inflow location
identification.
1001071 Also described herein are methods and systems for using the combined
or hybrid
approach to determining fluid inflow rates at the one or more locations at
which fluid inflow is
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identified. In these embodiments, the outputs of the fluid inflow
identification model and the
fluid inflow model can be used together to help to determine an inflow rate of
one or more fluids
and/or fluid phases within the wellbore. In some embodiments, the outputs of
the two models
can be combined to form a final flow rate determination. In some embodiments,
the fluid inflow
identification model can be used to identify the one or more locations at
which inflow is
occurring, and the fluid inflow model can then be used to determine the inflow
rates at the
identified locations, which can occur without combining the outputs of the two
models.
[00108] Figure 5 illustrates a flow chart for a method of determining fluid
inflow rates within a
wellbore 500. At step 502, the temperature features can be determined using
any of the
processes and systems as described herein. In some embodiments, a DTS system
can be used to
obtain distributed temperature sensing signal within the wellbore. The DTS
system can provide
distributed temperature measurements within the wellbore over time. A baseline
temperature
can be stored for the wellbore as described herein and used along with the
temperature
measurements to determine the temperature features. The temperature features
can include any
of those described herein including a depth derivative of temperature with
respect to depth, a
temperature excursion measurement, a baseline temperature excursion, a peak-to-
peak value, a
statistical measure of a variation with respect to time and/or distance, or a
combination thereof.
[00109] At step 504, one or more frequency domain features can be obtained
from an acoustic
signal originating within the wellbore. The frequency domain features can be
determined using
any of the processes and systems as described herein. In some embodiments, a
DAS system can
be used to obtain a distributed acoustic signal within the wellbore.
The acoustic signals
obtained from the DAS system can then be processed to determine one or more
frequency
domain features as described herein. In some embodiments, the frequency domain
features can
comprise at least one of: a spectral centroid, a spectral spread, a spectral
roll-off, a spectral
skewness, an RMS band energy, a total RMS energy, a spectral flatness, a
spectral slope, a
spectral kurtosis, a spectral flux, a spectral autocorrelation function, or
any combination thereof,
including combinations and modifications thereof.
[00110] The temperature features and the frequency domain features can then be
used to
determine a fluid inflow rate at one or more locations along the wellbore in
step 506. The
temperature features and the frequency domain features can be used in several
ways to obtain the
inflow rates into the wellbore. In some embodiments, the temperature features
can be used in the
33
Date Recue/Date Received 2022-10-27
fluid inflow identification model to obtain an identification of one or more
locations along the
wellbore having fluid inflow. Any of the models and methods of using the
temperature features
within the models as described herein can be used in step 506 to determine the
one or more
inflow locations. The output of the fluid inflow identification model can
provide an indication of
.. one or more locations along the wellbore having a fluid inflow event.
[00111] The frequency domain features can be used in the fluid inflow model to
obtain an
indication of the inflow rate for one or more fluids and/or fluid phases along
the wellbore. In
some embodiments, the inflow model can be limited to being executed at the one
or more
locations identified by the fluid inflow identification model. The inflow
model can then predict
the fluid inflow rates of one or more fluids and/or fluid phases at the one or
more locations. The
fluid inflow rates can then be representative of the fluid inflow rates at the
one or more locations
along the wellbore.
[00112] In some embodiments, the output of the fluid inflow identification
model and the fluid
inflow model can be combined to provide a combined output from the fluid
inflow identification
model and the fluid inflow model. The resulting combined output can then be
used to determine
a fluid inflow rate at the one or more locations along the wellbore as
identified by the fluid
inflow identification model. The combined output can be determined as a
function of the output
of the fluid inflow identification model and the output of the fluid inflow
model. Any suitable
functions can be used to combine the outputs of the two models. This can
include formulas,
products, averages, and the like, each of which can comprise one or more
constants or
weightings to provide the final output. The ability to determine the fluid
inflow rates as a
function of the output of both models can allow for either model to override
the output of the
other model. For example, if the fluid inflow identification model indicates
that a location along
the wellbore has a fluid inflow event, but the fluid inflow model indicates
little to no fluid
inflow, the resulting combined output may be considered to indicate that there
is no fluid inflow
at that location. Similarly, if the fluid inflow identification model
indicates a non-zero but low
likelihood of fluid inflow at a location, the output can serve as a weighting
to any fluid inflow
rates determined by the fluid inflow model. Thus, the use of the hybrid model
approach can
provide two separate ways to verify and determine the fluid inflow rates into
the wellbore.
[00113] The resulting output of the models can be an indication of fluid
inflow at one or more
locations along the wellbore. The fluid inflow prediction can be for one or
more fluids (e.g., a
34
Date Recue/Date Received 2022-10-27
gas, an aqueous liquid, a hydrocarbon liquid, etc.) and/or a fluid phase
(e.g., a gas phase, a liquid
phase, etc.). The fluid inflow rates can be used as indicated by the model in
their form as output
by the model. In some embodiments, the total fluid inflow rates can be
normalized across the
one or more locations having inflow. This can allow for a determination of a
relative proportion
of fluid flowing into the wellbore at each of the identified locations. This
can be useful for
understanding where the contributions to fluid flow are occurring along the
wellbore,
irrespective of the absolute flow rates within the wellbore.
[00114] In some embodiments, the fluid inflow rates can be refined by using a
measure of the
fluid flow rate from the wellbore as measured at logging tool above the
producing zones, a
wellhead, surface flow line, or the like. The fluid production rate can be
measured in step 508 by
a standard fluid flowrate measurement tool that is not associated with the
acoustic monitoring
system or the temperature monitoring system within the wellbore. For example,
the fluid
production rate can be measured with various flow meters. The fluid production
rate can
comprise an indication of the fluid flow rates of one or more fluids and/or
one or more fluid
phases.
[00115] The resulting fluid production rate information can then be combined
with the output of
the combined models as described herein. In some embodiments, the resulting
normalized fluid
inflow rates can be used with the actual production rates to allocate the
actual production rates
across the one or more inflow locations within the wellbore at step 510. As an
example, if the
models indicate that thirty percent of a liquid phase inflow rate is occurring
at a first location and
seventy percent is occurring at a second location, the actual production rate
can be allocated so
that thirty percent of the produced liquid phase flowrate is attributed to the
first location and the
remaining seventy percent of the liquid phase flow rate is flowing into the
wellbore at the second
location. The allocations can be made for one or more of the fluid inflow
rates and/or fluid
phase inflow rates, where the actual production rates for the fluids and/or
fluid phases can be
used with the corresponding model outputs for one or more fluids and/or fluid
phases. The
allocation process can allow for an improved accuracy for the determination of
fluid inflow rates
at the one or more locations along the wellbore.
[00116] Any of the systems and methods disclosed herein can be carried out on
a computer or
other device comprising a processor (e.g., a desktop computer, a laptop
computer, a tablet, a
server, a smartphone, or some combination thereof), such as the acquisition
device 160 of Figure
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disclosed herein such as the acquisition device or any portion thereof. The
computer system 680
includes a processor 682 (which may be referred to as a central processor unit
or CPU) that is in
communication with memory devices including secondary storage 684, read only
memory
(ROM) 686, random access memory (RAM) 688, input/output (I/0) devices 690, and
network
connectivity devices 692. The processor 682 may be implemented as one or more
CPU chips.
[00117] It is understood that by programming and/or loading executable
instructions onto the
computer system 680, at least one of the CPU 682, the RAM 688, and the ROM 686
are
changed, transforming the computer system 680 in part into a particular
machine or apparatus
having the novel functionality taught by the present disclosure. It is
fundamental to the electrical
engineering and software engineering arts that functionality that can be
implemented by loading
executable software into a computer can be converted to a hardware
implementation by well-
known design rules. Decisions between implementing a concept in software
versus hardware
typically hinge on considerations of stability of the design and numbers of
units to be produced
rather than any issues involved in translating from the software domain to the
hardware domain.
Generally, a design that is still subject to frequent change may be preferred
to be implemented in
software, because re-spinning a hardware implementation is more expensive than
re-spinning a
software design. Generally, a design that is stable that will be produced in
large volume may be
preferred to be implemented in hardware, for example in an application
specific integrated circuit
(ASIC), because for large production runs the hardware implementation may be
less expensive
than the software implementation_ Often a design may be developed and tested
in a software
form and later transformed, by well-known design rules, to an equivalent
hardware
implementation in an application specific integrated circuit that hardwires
the instructions of the
software. In the same manner as a machine controlled by a new ASIC is a
particular machine or
apparatus, likewise a computer that has been programmed and/or loaded with
executable
instructions may be viewed as a particular machine or apparatus.
[00118] Additionally, after the system 680 is turned on or booted, the CPU 682
may execute a
computer program or application. For example, the CPU 682 may execute software
or firmware
stored in the ROM 686 or stored in the RAM 688. In some cases, on boot and/or
when the
application is initiated, the CPU 682 may copy the application or portions of
the application from
the secondary storage 684 to the RAM 688 or to memory space within the CPU 682
itself, and
the CPU 682 may then execute instructions of which the application is
comprised. In some
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cases, the CPU 682 may copy the application or portions of the application
from memory
accessed via the network connectivity devices 692 or via the I/O devices 690
to the RAM 688 or
to memory space within the CPU 682, and the CPU 682 may then execute
instructions of which
the application is comprised. During execution, an application may load
instructions into the
CPU 682, for example load some of the instructions of the application into a
cache of the CPU
682. In some contexts, an application that is executed may be said to
configure the CPU 682 to
do something, e.g., to configure the CPU 682 to perform the function or
functions promoted by
the subject application. When the CPU 682 is configured in this way by the
application, the
CPU 682 becomes a specific purpose computer or a specific purpose machine.
[00119] The secondary storage 684 is typically comprised of one or more disk
drives or tape
drives and is used for non-volatile storage of data and as an over-flow data
storage device if
RAM 688 is not large enough to hold all working data. Secondary storage 684
may be used to
store programs which are loaded into RAM 688 when such programs are selected
for execution.
The ROM 686 is used to store instructions and perhaps data which are read
during program
execution. ROM 686 is a non-volatile memory device which typically has a small
memory
capacity relative to the larger memory capacity of secondary storage 684. The
RAM 688 is used
to store volatile data and perhaps to store instructions. Access to both ROM
686 and RAM 688
is typically faster than to secondary storage 684. The secondary storage 684,
the RAM 688,
and/or the ROM 686 may be referred to in some contexts as computer readable
storage media
and/or non-transitory computer readable media.
[00120] I/0 devices 690 may include printers, video monitors, electronic
displays (e.g., liquid
crystal displays (LCDs), plasma displays, organic light emitting diode
displays (OLED), touch
sensitive displays, etc.), keyboards, keypads, switches, dials, mice, track
balls, voice recognizers,
card readers, paper tape readers, or other well-known input devices.
[00121] The network connectivity devices 692 may take the form of modems,
modem banks,
Ethernet cards, universal serial bus (USB) interface cards, serial interfaces,
token ring cards,
fiber distributed data interface (FDDI) cards, wireless local area network
(WLAN) cards, radio
transceiver cards that promote radio communications using protocols such as
code division
multiple access (CDMA), global system for mobile communications (GSM), long-
term evolution
(LTE), worldwide interoperability for microwave access (WiMAX), near field
coimnunications
(NFC), radio frequency identity (RF1D), and/or other air interface protocol
radio transceiver
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cards, and other well-known network devices. These network connectivity
devices 692 may
enable the processor 682 to communicate with the Internet or one or more
intranets. With such a
network connection, it is contemplated that the processor 682 might receive
information from the
network, or might output information to the network (e.g., to an event
database) in the course of
performing the above-described method steps. Such information, which is often
represented as a
sequence of instructions to be executed using processor 682, may be received
from and outputted
to the network, for example, in the form of a computer data signal embodied in
a carrier wave.
[00122] Such information, which may include data or instructions to be
executed using
processor 682 for example, may be received from and outputted to the network,
for example, in
the form of a computer data baseband signal or signal embodied in a carrier
wave. The baseband
signal or signal embedded in the carrier wave, or other types of signals
currently used or
hereafter developed, may be generated according to several known methods. The
baseband
signal and/or signal embedded in the carrier wave may be referred to in some
contexts as a
transitory signal.
1001231 The processor 682 executes instructions, codes, computer programs,
scripts which it
accesses from hard disk, floppy disk, optical disk (these various disk based
systems may all be
considered secondary storage 684), flash drive, ROM 686, RAM 688, or the
network
connectivity devices 692. While only one processor 682 is shown, multiple
processors may be
present. Thus, while instructions may be discussed as executed by a processor,
the instructions
may be executed simultaneously, serially, or otherwise executed by one or
multiple processors.
Instructions, codes, computer programs, scripts, and/or data that may be
accessed from the
secondary storage 684, for example, hard drives, floppy disks, optical disks,
and/or other device,
the ROM 686, and/or the RAM 688 may be referred to in some contexts as non-
transitory
instructions and/or non-transitory information.
1001241 In an embodiment, the computer system 680 may comprise two or more
computers in
communication with each other that collaborate to perform a task. For example,
but not by way
of limitation, an application may be partitioned in such a way as to permit
concurrent and/or
parallel processing of the instructions of the application. Alternatively, the
data processed by the
application may be partitioned in such a way as to permit concurrent and/or
parallel processing
of different portions of a data set by the two or more computers. In an
embodiment,
virtualization software may be employed by the computer system 680 to provide
the
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functionality of a number of servers that is not directly bound to the number
of computers in the
computer system 680. For example, virtualization software may provide twenty
virtual servers
on four physical computers. In an embodiment, the functionality disclosed
above may be
provided by executing the application and/or applications in a cloud computing
environment
Cloud computing may comprise providing computing services via a network
connection using
dynamically scalable computing resources. Cloud computing may be supported, at
least in part,
by virtualization software. A cloud computing environment may be established
by an enterprise
and/or may be hired on an as-needed basis from a third party provider. Some
cloud computing
environments may comprise cloud computing resources owned and operated by the
enterprise as
well as cloud computing resources hired and/or leased from a third party
provider.
1001251 In an embodiment, some or all of the functionality disclosed above may
be provided as
a computer program product. The computer program product may comprise one or
more
computer readable storage medium having computer usable program code embodied
therein to
implement the functionality disclosed above. The computer program product may
comprise data
structures, executable instructions, and other computer usable program code.
The computer
program product may be embodied in removable computer storage media and/or non-
removable
computer storage media. The removable computer readable storage medium may
comprise,
without limitation, a paper tape, a magnetic tape, magnetic disk, an optical
disk, a solid state
memory chip, for example analog magnetic tape, compact disk read only memory
(CD-ROM)
disks, floppy disks, jump drives, digital cards, multimedia cards, and others.
The computer
program product may be suitable for loading, by the computer system 680, at
least portions of
the contents of the computer program product to the secondary storage 684, to
the ROM 686, to
the RAM 688, and/or to other non-volatile memory and volatile memory of the
computer system
680. The processor 682 may process the executable instructions and/or data
structures in part by
directly accessing the computer program product, for example by reading from a
CD-ROM disk
inserted into a disk drive peripheral of the computer system 680.
Alternatively, the processor
682 may process the executable instructions and/or data structures by remotely
accessing the
computer program product, for example by downloading the executable
instructions and/or data
structures from a remote server through the network connectivity devices 692.
The computer
program product may comprise instructions that promote the loading and/or
copying of data, data
structures, files, and/or executable instructions to the secondary storage
684, to the ROM 686, to
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the RAM 688, and/or to other non-volatile memory and volatile memory of the
computer system
680.
[00126] In some contexts, the secondary storage 684, the ROM 686, and the RAM
688 may be
referred to as a non-transitory computer readable medium or a computer
readable storage media.
A dynamic RAM embodiment of the RAM 688, likewise, may be referred to as a non-
transitory
computer readable medium in that while the dynamic RAM receives electrical
power and is
operated in accordance with its design, for example during a period of time
during which the
computer system 680 is turned on and operational, the dynamic RAM stores
information that is
written to it. Similarly, the processor 682 may comprise an internal RAM, an
internal ROM, a
cache memory, and/or other internal non-transitory storage blocks, sections,
or components that
may be referred to in some contexts as non-transitory computer readable media
or computer
readable storage media.
[00127] Having described various systems and methods, certain embodiments can
include, but
are not limited to systems and methods for determining inflow locations and
systems and
methods for determining inflow rates. Certain embodiments for determining
inflow locations
can include, but are not limited to:
[00128] In a first embodiment, a method of determining fluid inflow locations
comprises:
determining a plurality of temperature features from a distributed temperature
sensing signal
originating in a wellbore; using the plurality of temperature features in a
fluid inflow identification
model; and determining the presence of fluid inflow at one or more locations
along the wellbore
based on an output from the fluid inflow identification model.
[00129] A second embodiment can include the method of the first embodiment,
wherein the fluid
inflow is a liquid inflow at the one or more locations.
[00130] A third embodiment can include the method of the second embodiment,
wherein the
liquid inflow comprises an aqueous liquid, a hydrocarbon liquid, or a
combination of both.
[00131] A fourth embodiment can include the method of any one of the first to
third
embodiments, wherein the plurality of temperature features comprises a depth
derivative of
temperature with respect to depth.
[00132] A fifth embodiment can include the method of any one of the first to
fourth
embodiments, wherein the plurality of temperature features comprises a
temperature excursion
measurement, wherein the temperature excursion measurement comprises a
difference between a
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temperature reading at a first depth and a smoothed temperature reading over a
depth range,
wherein the first depth is within the depth range.
[00133] A sixth embodiment can include the method of any one of the first to
fifth embodiments,
wherein the plurality of temperature features comprises a baseline temperature
excursion, wherein
the baseline temperature excursion comprises a derivative of a baseline
excursion with depth,
wherein the baseline excursion comprises a difference between a baseline
temperature profile and a
smoothed temperature profile.
[00134] A seventh embodiment can include the method of any one of the first to
sixth
embodiments, wherein the plurality of temperature features comprises a peak-to-
peak value,
wherein the peak-to-peak value comprises a derivative of a peak-to-peak
difference with depth,
wherein the peak-to-peak difference comprises a difference between a peak high
temperature
reading and a peak low temperature reading with an interval.
[00135] An eighth embodiment can include the method of any one of the first to
seventh
embodiments, wherein the fluid inflow identification model comprises a
plurality of models,
wherein each model of the plurality of model uses one or more temperature
features of the plurality
of temperature features, and wherein determining the presence of the fluid
inflow comprises:
combining an output from each model to determine combined output; comparing
the combined
output with a fluid inflow threshold; and determining that the combined output
meets or exceeds
the fluid inflow threshold, wherein the determination of the presence of the
fluid inflow based on
the determination that the combined output meets or exceeds the fluid inflow
threshold.
[00136] A ninth embodiment can include the method of the eighth embodiment,
wherein one or
more of the plurality of models comprise multivariate models, and wherein the
output from each
multivariate model comprises an indication of a status of each temperature
feature with respect to a
multivariate normal distribution for the corresponding multivariate model.
[00137] A tenth embodiment can include the method of any one of the first to
eighth
embodiments, wherein the fluid inflow identification model uses an
unsupervised learning
algorithm.
1001381 An eleventh embodiment can include the method of any one of the first
to eighth
embodiments, wherein the fluid inflow identification model uses a supervised
learning algorithm.
[00139] A twelfth embodiment can include the method of any one of the first to
eleventh
embodiments, further comprising: receiving the distributed temperature sensing
signal from a
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sensor disposed in the wellbore, wherein the sensor comprises a fiber optic
based distributed
temperature sensor.
[00140] A thirteenth embodiment can include the method of any one of the first
to twelfth
embodiments, further comprising: determining one or more frequency domain
features of an
acoustic signal originating within the wellbore, wherein the acoustic signal
and the distributed
temperature sensing signal at least partially overlap in a position along the
wellbore; wherein
determining the presence of the fluid inflow uses the plurality of temperature
features and at least
one frequency domain feature of the one or more frequency domain features.
[00141] In a fourteenth embodiment, a method of determining fluid inflow
locations comprises:
determining a plurality of temperature features from a distributed temperature
sensing signal
originating in a wellbore, wherein the plurality of temperature features
comprise at least two of a
depth derivative of temperature with respect to depth, a temperature excursion
measurement, a
baseline temperature excursion, or a peak-to-peak value; and determining the
presence of fluid
inflow at one or more locations along the wellbore using the plurality of
temperature features.
[00142] A fifteenth embodiment can include the method of the fourteenth
embodiment, wherein
the fluid inflow is a liquid inflow at the one or more locations.
[00143] A sixteenth embodiment can include the method of the fifteenth
embodiment, wherein
the liquid inflow comprises an aqueous liquid, a hydrocarbon liquid, or a
combination of both an
aqueous liquid and a hydrocarbon liquid.
[00144] A seventeenth embodiment can include the method of any one of the
fourteenth to
sixteenth embodiments, wherein the temperature excursion measurement comprises
a difference
between a temperature reading at a first depth and a smoothed temperature
reading over a depth
range, wherein the first depth is within the depth range.
[00145] An eighteenth embodiment can include the method of any one of the
fourteenth to
seventeenth embodiments, wherein the baseline temperature excursion comprises
a derivative of a
baseline excursion with depth, wherein the baseline excursion comprises a
difference between a
baseline temperature profile and a smoothed temperature profile.
[00146] A nineteenth embodiment can include the method of any one of the
fourteenth to
eighteenth embodiments, wherein the peak-to-peak value comprises a derivative
of a peak-to-peak
difference with depth, wherein the peak-to-peak difference comprises a
difference between a peak
high temperature reading and a peak low temperature reading with an interval.
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[00147] In a twentieth embodiment, a system of determining fluid inflow
locations within a
wellbore comprises: a processor; a memory; and an analysis program stored in
the memory,
wherein the analysis program is configured, when executed on the processor,
to: receive a
distributed temperature sensing signal, wherein the distributed temperature
sensing signal
originates within the wellbore; determine a plurality of temperature features
from the distributed
temperature sensing signal; use the plurality of temperature features in a
fluid inflow identification
model; and determine the presence of fluid inflow at one or more locations
along the wellbore
based on an output from the fluid inflow identification model.
[00148] A twenty first embodiment can include the system of the twentieth
embodiment, wherein
the fluid inflow is a liquid inflow at the one or more locations.
[00149] A twenty second embodiment can include the system of the twenty first
embodiment,
wherein the liquid inflow comprises an inflow rate for an aqueous liquid, a
hydrocarbon liquid, or a
combination of both.
[00150] A twenty third embodiment can include the system of any one of the
twentieth to twenty
second embodiments, wherein the plurality of temperature features comprises a
depth derivative of
temperature with respect to depth.
[00151] A twenty fourth embodiment can include the system of any one of the
twentieth to twenty
third embodiments, wherein the plurality of temperature features comprises a
temperature
excursion measurement, wherein the temperature excursion measurement comprises
a difference
between a temperature reading at a first depth and a smoothed temperature
reading over a depth
range, wherein the first depth is within the depth range.
[00152] A twenty fifth embodiment can include the system of any one of the
twentieth to twenty
fourth embodiments, wherein the plurality of temperature features comprises a
baseline
temperature excursion, wherein the baseline temperature excursion comprises a
derivative of a
baseline excursion with depth, wherein the baseline excursion comprises a
difference between a
baseline temperature profile and a smoothed temperature profile.
[00153] A twenty sixth embodiment can include the system of any one of the
twentieth to
twenty fifth embodiments, wherein the plurality of temperature features
comprises a peak-to-peak
value, wherein the peak-to-peak value comprises a derivative of a peak-to-peak
difference with
depth, wherein the peak-to-peak difference comprises a difference between a
peak high
temperature reading and a peak low temperature reading with an interval.
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[00154] A twenty seventh embodiment can include the system of any one of the
twentieth to
twenty sixth embodiments, wherein the fluid inflow identification model
comprises a plurality of
models, wherein each model of the plurality of model uses one or more
temperature features of the
plurality of temperature features, and wherein the analysis program is further
configured to:
combine an output from each model to determine combined output; compare the
combined output
with a fluid inflow threshold; and determine that the combined output meets or
exceeds the fluid
inflow threshold, wherein the determination of the presence of the fluid
inflow based on the
determination that the combined output meets or exceeds the fluid inflow
threshold.
[00155] A twenty eighth embodiment can include the system of the twenty
seventh embodiment,
wherein one or more of the plurality of models comprise multivariate models,
and wherein the
output from each multivariate model comprises an indication of a status of
each temperature
feature with respect to a multivariate normal distribution for the
corresponding multivariate model.
[00156] A twenty ninth embodiment can include the system of any one of the
twentieth to twenty
seventh embodiments, wherein the fluid inflow identification model uses an
unsupervised learning
algorithm.
[00157] A thirtieth embodiment can include the system of any one of the
twentieth to twenty
seventh embodiments, wherein the fluid inflow identification model uses a
supervised learning
algorithm.
[00158] A thirty first embodiment can include the system of any one of the
twentieth to thirtieth
embodiments, wherein the analysis program is further configured to: receive
the distributed
temperature sensing signal from a sensor disposed in the wellbore, wherein the
sensor comprises a
fiber optic based distributed temperature sensor.
[00159] A thirty second embodiment can include the system of any one of the
twentieth to thirty
first embodiments, wherein the analysis program is further configured to:
determine one or more
frequency domain features of an acoustic signal originating within the
wellbore, wherein the
acoustic signal and the distributed temperature sensing signal_ at least
partially overlap in a position
along the wellbore; and determine the presence of the fluid inflow using the
plurality of
temperature features and at least one frequency domain feature of the one or
more frequency
domain features.
[00160] Certain embodiments for determining inflow rates can include, but are
not limited to:
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[00161] In a first embodiment, a method of determining fluid inflow rates
within a wellbore
comprises: determining a plurality of temperature features from a distributed
temperature sensing
signal originating in a wellbore; determining one or more frequency domain
features from an
acoustic signal originating the wellbore; and using at least one temperature
feature of the plurality
of temperature features and at least one frequency domain feature of the one
or more frequency
domain features to determine a fluid inflow rate at one or more locations
along the wellbore.
[00162] A second embodiment can include the method of the first embodiment,
wherein using the
at least one temperature feature and the at least one frequency domain feature
comprises: using the
at least one temperature features in a first fluid inflow model; using at
least one frequency domain
feature of the one or more frequency domain features in a second fluid inflow
model; combining
an output from the first fluid inflow model and an output from the second
fluid inflow model to
form a combined output; and determining a fluid inflow rate at the one or more
locations along the
wellbore based on the combined output.
[00163] A third embodiment can include the method of the second embodiment,
wherein the first
fluid inflow model comprise one or more multivariate models, and wherein the
output from each
multivariate model of the one or more multivariate model comprises an
indication of the one or
more locations along the wellbore.
1001641 A fourth embodiment can include the method of the third embodiment,
wherein the
second fluid inflow model comprises a regression model, and wherein the output
from the
regression model comprises an indication of a fluid inflow rate at the one or
more locations along
the wellbo re.
[00165] A fifth embodiment can include the method of the fourth embodiment,
wherein
combining the output from the first fluid model with the output from the
second fluid inflow model
comprises determining the combined output as a function of: 1) the output from
the first fluid
model, and 2) the output from the second fluid inflow model.
[00166] A sixth embodiment can include the method of any one of the first to
fifth embodiments,
further comprising: receiving an indication of a fluid flow rate from the
wellbore; and allocating a
portion of the fluid flow rate from the wellbore to the one or more locations
along the wellbore
based on the determined fluid inflow rate at the one or more locations based
on the combined
output.
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[00167] A seventh embodiment can include the method of any one of the first to
sixth
embodiments, wherein the fluid inflow rate is the liquid inflow rate at one or
more locations along
the wellbore.
[00168] An eighth embodiment can include the method of any one of the first to
seventh
embodiments, wherein the plurality of temperature features comprise at least
two of a depth
derivative of temperature with respect to depth, a temperature excursion
measurement, a baseline
temperature excursion, or a peak-to-peak value.
[00169] A ninth embodiment can include the method of any one of the first to
eighth
embodiments, wherein the temperature excursion measurement comprises a
difference between a
temperature reading at a first depth and a smoothed temperature reading over a
depth range,
wherein the first depth is within the depth range.
[00170] A tenth embodiment can include the method of any one of the first to
ninth embodiments,
wherein the baseline temperature excursion comprises a derivative of a
baseline excursion with
depth, wherein the baseline excursion comprises a difference between a
baseline temperature
profile and a smoothed temperature profile.
[00171] An eleventh embodiment can include the method of any one of the first
to tenth
embodiments, wherein the peak-to-peak value comprises a derivative of a peak-
to-peak difference
with depth, wherein the peak-to-peak difference comprises a difference between
a peak high
temperature reading and a peak low temperature reading with an interval.
[00172] A twelfth embodiment can include the method of any one of the first to
eleventh
embodiments, wherein the one or more frequency domain features comprise at
least one of: a
spectral centroid, a spectral spread, a spectral roll-off, a spectral
skewness, an RIVIS band energy, a
total RIVIS energy, a spectral flatness, a spectral slope, a spectral
kurtosis, a spectral flux, or a
spectral autocorrelation function.
[00173] In a thirteenth embodiment, a method of determining fluid inflow rates
along a wellbore
comprises: determining a plurality of temperature features from a distributed
temperature sensing
signal originating in a wellbore, wherein the plurality of temperature
features comprise at least two
of: a depth derivative of temperature with respect to depth, a temperature
excursion measurement,
a baseline temperature excursion, or a peak-to-peak value; determining one or
more frequency
domain features from an acoustic signal originated in the wellbore; and using
at least one
temperature feature of the plurality of temperature features and at least one
frequency domain
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feature of the one or more frequency domain features to determine a fluid
inflow rate at one or
more locations along the wellbore.
[00174] A fourteenth embodiment can include the method of the thirteenth
embodiment, wherein
the temperature excursion measurement comprises a difference between a
temperature reading at a
first depth and a smoothed temperature reading over a depth range, wherein the
first depth is within
the depth range.
[00175] A fifteenth embodiment can 'include the method of the thirteenth or
fourteenth
embodiment, wherein the baseline temperature excursion comprises a derivative
of a baseline
excursion with depth, wherein the baseline excursion comprises a difference
between a baseline
temperature profile and a smoothed temperature profile.
1001761 A sixteenth embodiment can include the method of any one of the
thirteenth to fifteenth
embodiments, wherein the peak-to-peak value comprises a derivative of a peak-
to-peak difference
with depth, wherein the peak-to-peak difference comprises a difference between
a peak high
temperature reading and a peak low temperature reading with an interval.
[00177] A seventeenth embodiment can include the method of any one of the
thirteenth to
sixteenth embodiments, wherein the one or more frequency domain features
comprise at least one
of: a spectral centroid, a spectral spread, a spectral roll-off, a spectral
skewness, an RMS band
energy, a total RIVIS energy, a spectral flatness, a spectral slope, a
spectral kurtosis, a spectral flux,
or a spectral autocorrelation function.
[00178] An eighteenth embodiment can include the method of any one of the
thirteenth to
seventeenth embodiments, wherein using the at least one temperature feature
and the at least one
frequency domain feature comprises: using the at least one temperature
features in a first fluid
inflow model; using at least one frequency domain feature of the one or more
frequency domain
features in a second fluid inflow model; combining an output from the first
fluid inflow model and
an output from the second fluid inflow model to form a combined output; and
determining a fluid
inflow rate at the one or more locations along the wellbore based on the
combined output.
[00179] A nineteenth embodiment can include the method of the eighteenth
embodiment, wherein
the first fluid inflow model comprise one or more multivariate models, and
wherein the output
from each multivariate model of the one or more multivariate model comprises
an indication of the
one or mow locations along the wellbore.
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[00180] A twentieth embodiment can include the method of the nineteenth
embodiment, wherein
the second fluid inflow model comprises a regression model, and wherein the
output from the
regression model comprises an indication of a fluid inflow rate at the one or
more locations along
the wellbo re.
[00181] A twenty first embodiment can include the method of the twentieth
embodiment, wherein
combining the output from the first fluid model with the output from the
second fluid inflow model
comprises determining the combined output as a function of: 1) the output from
the first fluid
model, and 2) the output from the second fluid inflow model.
[00182] A twenty second embodiment can include the method of any one of the
thirteenth to
twenty first embodiments, further comprising: receiving an indication of a
fluid flow rate from the
wellbore; and allocating a portion of the fluid flow rate from the wellbore to
the one or more
locations along the wellbore based on the detemiined fluid inflow rate at the
one or more locations
based on the combined output.
[00183] In a twenty third embodiment, a system of determining fluid inflow
rates within a
wellbore comprises: a processor; a memory; and an analysis program stored in
the memory,
wherein the analysis program is configured, when executed on the processor,
to: receive a
distributed temperature sensing signal and an acoustic signal, wherein the
temperature sensing
signal and the acoustic signal originated within the wellbore; determine a
plurality of temperature
features from the distributed temperature sensing signal; determine one or
more frequency domain
features from the acoustics signal; and determine a fluid inflow rate at one
or more locations along
the wellbore using at least one temperature feature of the plurality of
temperature features and at
least one frequency domain feature of the one or more frequency domain
features.
[00184] A twenty fourth embodiment can include the system of the twenty third
embodiment,
wherein the analysis program is further configured to: use the at least one
temperature features in a
first fluid inflow model; use at least one frequency domain feature of the one
or more frequency
domain features in a second fluid inflow model; combine an output from the
first fluid inflow
model and an output from the second fluid inflow model to form a combined
output; and determine
a fluid inflow rate at the one or more locations along the wellbore based on
the combined output.
[00185] A twenty fifth embodiment can include the system of the twenty fourth
embodiment,
wherein the first fluid inflow model comprise one or more multivariate models,
and wherein the
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output from each multivariate model of the one or more multivariate model
comprises an
indication of the one or more locations along the wellbore.
[00186] A twenty sixth embodiment can include the system of the twenty fifth
embodiment,
wherein the second fluid inflow model comprises a regression model, and
wherein the output from
the regression model comprises an indication of a fluid inflow rate at the one
or more locations
along the wellbore.
[00187] A twenty seventh embodiment can include the system of the twenty sixth
embodiment,
wherein the analysis program is further configured to: combine the output from
the first fluid
model with the output from the second fluid inflow model as a function of: 1)
the output from the
first fluid model, and 2) the output from the second fluid inflow model.
[00188] A twenty eighth embodiment can include the system of any one of the
twenty third to
twenty seventh embodiments, wherein the analysis program is further configured
to: receive an
indication of a fluid flow rate from the wellbore; and allocate a portion of
the fluid flow rate from
the wellbore to the one or more locations along the wellbore based on the
determined fluid inflow
rate at the one or more locations based on the combined output.
[00189] A twenty ninth embodiment can include the system of any one of the
twenty third to
twenty eighth embodiments, wherein the fluid inflow rate is the liquid inflow
rate at one or more
locations along the wellbore.
[00190] A thirtieth embodiment can include the system of any one of the twenty
third to twenty
ninth embodiments, wherein the plurality of temperature features comprise at
least two of a depth
derivative of temperature with respect to depth, a temperature excursion
measurement, a baseline
temperature excursion, or a peak-to-peak value.
[00191] A thirty first embodiment can include the system of any one of the
twenty third to
thirtieth embodiments, wherein the temperature excursion measurement comprises
a difference
between a temperature reading at a first depth and a smoothed temperature
reading over a depth
range, wherein the first depth is within the depth range.
[00192] A thirty second embodiment can include the system of any one of the
twenty third to
thirty first embodiments, wherein the baseline temperature excursion comprises
a derivative of a
baseline excursion with depth, wherein the baseline excursion comprises a
difference between a
baseline temperature profile and a smoothed temperature profile.
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[00193] A thirty third embodiment can include the system of any one of the
twenty third to thirty
second embodiments, wherein the peak-to-peak value comprises a derivative of a
peak-to-peak
difference with depth, wherein the peak-to-peak difference comprises a
difference between a peak
high temperature reading and a peak low temperature reading with an interval.
[00194] A thirty fourth embodiment can include the system of any one of the
twenty third to thirty
third embodiments, wherein the one or more frequency domain features comprise
at least one of a
spectral centroid, a spectral spread, a spectral roll-off, a spectral
skewness, an RMS band energy, a
total RMS energy, a spectral flatness, a spectral slope, a spectral kurtosis,
a spectral flux, or a
spectral autocorrelation function.
[00195] The embodiments disclosed herein have included systems and methods for
detecting
and/or characterizing sand ingress and/or sand transport within a subterranean
wellbore, or a
plurality of such wellbores. Thus, through use of the systems and methods
described herein, one
may more effectively limit or avoid sand ingress and accumulation with a
wellbore so as to
enhance the economic production therefrom.
[00196] While exemplary embodiments have been shown and described,
modifications thereof
can be made by one skilled in the art without departing from the scope or
teachings herein. The
embodiments described herein are exemplary only and are not limiting. Many
variations and
modifications of the systems, apparatus, and processes described herein are
possible and are
within the scope of the disclosure. Accordingly, the scope of protection is
not limited to the
embodiments described herein, but is only limited by the claims that follow,
the scope of which
shall include all equivalents of the subject matter of the claims. Unless
expressly stated
otherwise, the steps in a method claim may be performed in any order. The
recitation of
identifiers such as (a), (b), (c) or (1), (2), (3) before steps in a method
claim are not intended to
and do not specify a particular order to the steps, but rather are used to
simplify subsequent
reference to such steps.
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